modeling and analysis methods to improve industrial …gaujalg/hdr.pdf · 2011-09-29 · institut...

INSTITUT POLYTECHNIQUE DE GRENOBLE

MODELING AND ANALYSIS METHODS TO IMPROVE INDUSTRIALPERFORMANCE

A THESISpresented by

Gulgun Alpan

in partial fulfillment ofthe requirements for the degree

Habilitation a Diriger des Recherches

JURY

M. Jean-Pierre Campagne INSA de Lyon RapporteurM. Chengbin Chu Ecole Centrale de Paris ExaminateurMme. Maria Di-Mascolo CNRS, G-SCOP, Grenoble RapporteurM. Alexandre Dolgui Ecole des Mines de Saint-Etienne ExaminateurM. Michel Gourgand ISIMA, Clermont-Ferrand RapporteurM. Bernard Penz Grenoble INP Examinateur

Contents

1 Introduction 5

1.1 Assembly line flexibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.2 Internal operations in cross docking . . . . . . . . . . . . . . . . . . . . . . . . 6

1.3 Ramp-up phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4 Supply chain operational risks . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2 Flexibility 11

2.1 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Where do we stand in the literature? . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.1 Industrial context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.2 Related car sequencing literature . . . . . . . . . . . . . . . . . . . . . 13

2.2.3 Measuring difficulties to obtain a car sequence . . . . . . . . . . . . . 13

2.2.4 Evaluating the flexibility . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3 Solution Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3.1 Defining spacing constraints for car sequencing . . . . . . . . . . . . . 16

2.3.2 Identifying difficulties to generate a car sequence . . . . . . . . . . . . 24

2.3.3 Measuring the flexibility of the assembly line . . . . . . . . . . . . . . 26

2.4 Major Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3 Cross docking 33

3.1 Research objectives and our place in the literature . . . . . . . . . . . . . . . 33

3.2 Problem description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.3 Solution Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.3.1 Single receiving and single shipping door cross docks . . . . . . . . . . 38

3.3.2 Multi-door cross docks . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.3.3 Other types of problems . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.4 Major contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4 Ramp-up 57

4.1 Research objectives and our place in the literature . . . . . . . . . . . . . . . 57

4.1.1 Industrial Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4.1.2 Where do we stand in the literature? . . . . . . . . . . . . . . . . . . . 58

4.1.3 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.2 Solution methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

4.2.1 Major ramp-up problem types in a low volume industry . . . . . . . . 61

4.2.2 Auditing tools to characterize interfaces . . . . . . . . . . . . . . . . . 64

i

ii CONTENTS

4.3 Additional studies on new product introduction and ramp-up management . 694.4 Major contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

5 Supply chains risk management 755.1 Research objectives and our place in the literature . . . . . . . . . . . . . . . 755.2 Solution Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

5.2.1 A high level Petri net based model . . . . . . . . . . . . . . . . . . . . 775.2.2 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795.2.3 Object Oriented Design approach for PN modeling . . . . . . . . . . . 815.2.4 Coordination mechanism as a risk mitigation among supply chain partners 83

5.3 Major contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

6 Conclusion and Perspectives 896.1 Perspectives in automobile industry . . . . . . . . . . . . . . . . . . . . . . . 896.2 Perspectives in cross docking . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

6.2.1 Relaxing some assumptions . . . . . . . . . . . . . . . . . . . . . . . . 906.2.2 Improving heuristic models . . . . . . . . . . . . . . . . . . . . . . . . 916.2.3 Some long term perspectives . . . . . . . . . . . . . . . . . . . . . . . 92

6.3 Perspectives in ramp-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 926.4 Perspectives in supply chain risk management . . . . . . . . . . . . . . . . . . 94

Acknowledgments

The research findings presented in this document are the results of team work and collabo-ration with colleagues and students. I would like to thank them all since the research workwouldn’t have been as ample and as delightful without the input of each one of them. I willnot enumerate the list of collaborations here. Nevertheless, I would like to send some specialthanks to Yannick Frein and Bernard Penz for their constant support and helpful advice.

I would like to thank the members of my jury for accepting to evaluate and comment thisdocument. I will take great care of their valuable comments.

Finally, I would like to thank my little ones, Theo Can, Martin Alp, Mia Su and mysoulmate Bruno for their unconditional love and ever-lasting support and encouragement.

1

2 CONTENTS

Preface

My educational background, both in undergraduate and graduate levels, is in industrial en-gineering. During my masters and doctoral studies, I have oriented my research aroundsupervision and control of complex systems. This research area is highly anchored in the dis-crete event systems community and attracts researchers, in majority, from computer scienceand electrical engineering and partially from industrial engineering. I continued working inthis domain until September 2003, date of my arrival at the Institut Polytechnique de Greno-ble, school of Industrial Engineering and G-SCOP laboratories (ex-GILCO). Since then, Iam working on problems related to industrial performance, for a single entity or networkedentities in their supply chains. These types of problems are typically studied by industrialengineering and operations research communities. Even though the problems are different inuniverse of supervision and control and the supply chain management, most of the modelingand analysis tools are transposable in these two research areas.

In this document, I made the choice of detailing my research activities since September2003, for two reasons: First of all, almost all of my masters and PhD thesis supervisionsare in this period. Secondly, my new research activities are progressively distancing me fromsupervision and control.

This document is accompanied by:

• My curriculum vitae which traces all my research and teaching activities.

• An extended summary of this document in french.

For the interested reader, our major journal articles are accessible on my personal webpage at the following address: http://www.g-scop.fr/∼gaujalg

I wish you a pleasant reading.

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4 CONTENTS

Chapter 1

Introduction

Every time, I have a chance to dive into the complex world of industries with my researcher’ssuit, I feel like I discover the cave of the Forty Thieves in the tale of Ali Baba. The problemsthat are faced are often very complex and when formalized in a researcher’s mind some maylead to very rich research problems. And it is not enough to spell the magical words, OpenSesame!, to crack them up.

In this introductory chapter I will present some of the industrial challenges we have stud-ied. Some of these challenges are directly encountered in field studies at industrial sites,and some others are theoretical works that are applied in an industrial context, later on.Whatever the starting point is, we can find their roots in recent developments of industrialenvironments.

For the last few decades, the industries are evolving in an environment which seems tobe in a perpetual movement. The objectives of a company has not changed much. The aimis still to produce goods or services with the right quality, at the right time and price, withsome margin for profit. What is changing in a gallop pace, however, is the environment inwhich they produce. Therefore, the way the companies attain the objectives is evolving veryfast as well.

For instance, the globalization has greatly influenced the business models of enterprises.To stay competitive, a global company has to be cost-effective, fast responding, capable ofinnovating, be present in the international scene, extend his supply chain both for capturingnew markets and finding new supply sources, etc. Stemming from a single term such asglobalization, we observe a sequence of chain reactions and new industrial challenges. We canobserve similar chain reactions if we take another term. For instance, global heating will callup for green supply chains, sustainable production, etc. or technological advances will call upfor the use of RFID in supply chain management, information exchange among supply chainpartners, etc. And there you go: welcome to the cave of Forty Thieves! There is a jewel tofind for everyone.

Below are some research grounds we have found since 2003 in this huge excavation area. Iregrouped them under the umbrella of industrial performance since the way they are handledhas an influence on the performance measures of the companies. As you will see, each one ofthem opens up interesting investigation areas in research. Our objective is to develop methodsand tools for the analysis and decision making in the highly complex world of one or severalnetworked companies.

5

6 CHAPTER 1. INTRODUCTION

1.1 Assembly line flexibility

One of the early mutations in industrial environment is the exchange of roles of the customersand the producers. During the first half of the 20th century, the market was governed by thesupply. The products were standardized to satisfy a typical customer’s requirements andproduced in mass, providing economies of scale. This practice has long been replaced by pullsystems. The market is now governed by demand and customer needs play an importantrole in a company’s offer. One of the consequences is the emergence of product diversity. Toattract customer demand and capture market share, the companies offer an increased amountof product varieties to customers.

An important impact of product diversity is on the organization of the production. Whenthe production is pulled by the demand and the products are highly customized, one way tosatisfy this demand on time is to install some flexibility in the production system. Flexibilityis also used to cope up with uncertainties of the customer demand or unexpected events whichmay occur during production.

Automobile industry is one sector which offers high product diversity to the end customers.Our studies related to production flexibility is carried out within the context of the automobileindustry, in collaboration with the group PSA Peugeot-Citroen. In this context, the termflexibility refers to the use of extra resources to increase the production capacity in terms ofnumber of vehicles produced.

The challenge here is to be able to measure the flexibility which is set up in the systemto make sure that it is correctly dimensioned. If the flexibility is too loose, unnecessary costwill be incurred. If it is not enough, profits may drop down.

In this domain, we have developed methods and tools to measure the level of flexibilityavailable on the assembly line. We were particularly interested in generation of car sequences.In the case of high diversity, car sequencing helps smoothing the workload on the assemblyline and hence impacts the required flexibility. We have proposed a method to define spacingconstraints that are used as input data to car sequencing tools. The method based on thisdefinition prove to be efficient in identifying delays that are accumulated due to inappropriatecar sequences. Difficulty of obtaining a car sequence partially depends on the correlationsbetween spacing constraints. We have also provided tools which can give an a priori evaluationon the level of difficulty to generate a car sequence which satisfies all spacing constraints.Chapter 2 describes in detail these methods and our major research findings.

1.2 Internal operations in cross docking

Worldwide extension of markets and strategical decisions such as fusion, joint venture oracquisition result in an increase in number of supply chain partners as well as intermediaryoperators between producing companies and consumers. Faced with this complexity of supplychains, the companies are seeking to improve their performance in logistics. Logistic opera-tions are typically non-value added operations. They generate costs yet they are necessaryfor a good quality of service to the customers. Therefore, on one hand, the companies arewilling to increase the speed of logistic operations and, on the other hand, to reduce the costsgenerated. One way to reach this goal is an optimal management of procurement and thedistribution processes. Cross docking proves to be an interesting logistics solution to this end.

Cross docking is a logistic technique which seeks to reduce the inventory holding, or-

1.3. RAMP-UP PHASE 7

Suppliers

Clients

Distribution by vehicule routing

Suppliers

Clients

Clients

Suppliers

Direct distribution from suppliers to clients

cross docking

Distribution via Cross docking

Figure 1.1: Distribution with or without cross docking

der picking, transportation costs as well as the delivery time. A platform of cross docking(referred to as cross dock, in this document) is a consolidation point of inbound products.Materials arriving to the cross dock from suppliers are unloaded from the inbound trailers,sorted according to their destinations, possibly consolidated with other products to the samedestination and reloaded into an outbound trailer. Hence, as seen in figure 1.1, the companiesmake use of economies of scale and reduction of high-distance distribution channels observedin vehicle routing or direct distribution.

In a cross dock, the above mentioned operations usually take place within less than twentyfour hours. Storage is avoided as much as possible or is minimized to a great extent. There-fore, this technique is especially popular in the transportation industries (e.g. UPS, Postalservices,...) or in the distribution of the perishable products (e.g. Danone, Casino,...) butis also used in other sectors (e.g. Schneider Electric, Hewlett Packard, Kmart, ...) for itsefficiency in time and cost. In summary, cross docking can be considered as a lean logistic so-lution since some common wastes seen in distribution industry can be avoided: clients can beserved more often with full truck loads, storage is limited, truck travel distances are reduced,etc.

The daily challenge at a cross dock is to orchestrate the operations so that everything workslike a swiss watch: The clients are delivered on time with minimum cost. Our research work inthis domain attacks this challenge. We propose a series of models to schedule transshipmentoperations in cross docks so that the operational costs are minimized. Several cross docksettings are studied to this end, varying from single receiving and shipping door docks tomulti-dock environments. Some of these models give optimum solutions while some seek fornear optimal ones. We also explored the effects of having partial or no information on inboundarrivals on the performance of the cross dock. In chapter 3, I will present our research findingsin this domain.

1.3 Ramp-up phase

For modern enterprises, a powerful tool to overpass the competitors is the capacity to innovatetheir products and/or services. New products are central to a company’s profitability: amongthe best performing firms, 49% of sales are derived from new products [DB99].

The continuous innovation which has been a challenge especially for high technologi-cal products is now common in all industrial sectors. This means that, new products areintroduced on the shop floor more often. Products which are developed in a small scale,laboratory-like environment will be transferred into a high-volume production environment.


For these newly developed products, the production as well as the supply chain processes arenot as smooth as for the mature products. There is a buffer period between completion ofdevelopment and full capacity utilization of the production system. The beginning of thisperiod is marked by the commercial production of a new product and ends when the produc-tion reaches to a maturity. In the life-cycle of a product, this period is commonly called theramp-up period or production launch.

This is a crucial phase in the life cycle of a new product for several reasons:

• Time: The first entrants on the market can profit from premium prices until thecompetitors develop similar products. There is a relationship between the length ofthe start-up phase and the final level of economical efficiency and a part of the profitcan be lost as a result of a late product launch.

• Cost: Production ramp-up is considered as a major cost driver. Production equipmentis increasingly expensive and product life cycles are shorter. Hence, pressure on fastprofitability is higher.

• Complexity: The growing complexity of high tech products and strong outsourcingrender the production systems and the supply chains to be more complex as well. Theperformance during the ramp-up relies heavily on how well the supply chain and theproduction system have been set up for the new product.

The challenge in this domain is inherent to the characteristics of the ramp-up phase whichdistinguish it from the mature production phase. Due to high uncertainty which envelops theproduct and the process, the occurrence frequency of discrepancies as well as their variety isalso much higher than for a mature product. For that reason, the management techniqueswhich are appropriate during the stable mature production phase do not perform well forunstable ramp-up phase.

Our industrial partner for this research area is Siemens. Most of our studies in this domainis backed up by case studies conducted at different production sites of this company. Someof these case studies are used to identify the problem types encountered during the ramp-upphase, in the context of a low volume and high diversity industry. One of these problemsis the lack of communication and difficulties in information exchange during the ramp-upphase. We have proposed a set of auditing tools which help identifying critical interactionsamong the actors who are involved in this phase. Finding of these auditing tools are useful toconfigure information exchange structure in the upcoming projects. In chapter 4, I will firstpresent our findings in this area.

1.4 Supply chain operational risks

As we mentioned before, the modern supply chains are more and more complex, with manyactors operating in network. The increased size of these structures as well as the type ofrelations which exist among the supply-chain partners generate a certain dependency amongcompanies. A disruption somewhere in this network has effects on the rest of the partners.

Furthermore, the firms seek to increase their competitive edge by employing new strategiessuch as re-centering some of their activities by outsourcing, leaning up their supply chain byremoving any redundancies in the supply network, transferring activities to off-shore countriesto reduce costs, and so on. Even though efficient in a stable environment, these strategies

1.4. SUPPLY CHAIN OPERATIONAL RISKS 9

further augment the vulnerabilities of firms in an uncertain environment, thus resulting inrisks to take into account.

The supply chain risks management is a very popular research area. The challenges arenumerous and stretches from identification of supply chain risks to the control and mitiga-tion of these risks. Our research is mostly oriented on how we can evaluate risks and theconsequences of possible mitigation actions to reduce the damage caused by risks. Here, therisks such as terrorist attacks, instability of currency exchanges, natural disasters, etc. arenot considered. The extended supply chains continue to be vulnerable to operational riskssuch as machine breakdowns at the suppliers, loss of communication links with the supplychain partners, demand fluctuations, etc.

In this domain, we have proposed a method based on Petri net modeling and simulation.Our method is capable of representing several supply chain operational risks on a single model.The model is quite modular and can be used to incorporate many supply chain partners atthe same time. An industrial case study is used to test the capacity of the model to solve realproblems. I will present our contributions in this domain in chapter 5.

The following chapters are organized so that the readers can see what the research ob-jectives are, how our works are positioned in the scientific literature, the type of models wepropose, and our major contributions in the domain. Almost all of the proposed models areaccompanied by numerical tests. These numerical tests are not included in the documentbut are accessible in our articles. Here, I just convey the major observations we have madeout of these experiments. At the end of each chapter, the reader can find a list of our ownpublications in the domain. The global bibliography which regroups the references from otherresearchers are given at the end of the document. In the text, our articles are cited usingnumber citation format, while the closely related research work from global bibliography iscited in alpha-numerical format.

Chapter 2

Flexibility issues in automobileindustry

In this chapter, we present the research work realized in collaboration with Yannick Frein,Aymeric Lesert [Les06], Olivier Briant, Radwan El-Hadj Khalaf [EHK06] and Marcelo Ferioli[Fer06]. The related publications are as follows: ([6, 7, 2, 3, 4, 5, 1]).

During the realization of these research works, we had strong collaboration with theautomobile constructor, PSA Peugeot-Citroen, either in terms of CIFRE or research contracts.Modeling and analysis tools resulting from our models are currently applied in this company.

2.1 Research objectives

One of the major activities of an automobile constructor is the final assembly process. Everymonth, the assembly line is dimensioned to produce a certain number of vehicles. Thesepreparation activities are not easy tasks. First of all, the product diversity is extremely highand each product has different resource requirements. The assembly line is often composedof several hundreds of workstations. Therefore, daily scheduling process is quite complicated.Secondly, the market is dynamic hence the forecast errors may occur. Finally, like in manyindustries, unpredictable events, such as machine break downs or supply problems may occur.To take into account these difficulties, the assembly line is dimensioned with some extraproduction capacity. In our studies, this extra production capacity translates the flexibilityof the assembly line. If the assembly line is dimensioned only to produce what is forecastedfor the month, then the line has no flexibility. Any unexpected event or forecast errors willresult in lost profits. On the other hand, if it is over-dimensioned extra resources which areallocated will not be used efficiently, resulting in extra costs.

Our target here was to determine and evaluate the available flexibility on the assemblyline. In our industrial context, the flexibility was put in place to overcome any difficultiesresulting from one of the following sources: (i) Forecast errors, (ii) Unexpected events and(iii) difficulties to obtain a good car sequence. Our studies focused only on the third point.Indeed, our statistical analysis on several production sites over a 1 year period has provedthat the forecasts were reliable enough. Furthermore, at the time of the study, the companywas deploying a company-wide program aiming to reduce the number of unexpected events.

As a result, we developed 3 lines of actions:

11

12 CHAPTER 2. FLEXIBILITY

Figure 2.1: Top view of a workstation in a paced assembly line

• How to take into account the operational difficulties observed on the assembly line whengenerating the car sequence.

• Given a set of constraints, how to know, a priori, if obtaining a car sequence is easy ordifficult.

• And finally how to evaluate the flexibility available on the assembly line.

2.2 Where do we stand in the literature?

In order to answer the question raised above, I have to first present the industrial contextwhich highly effects the way our research is conducted and its place in the scientific literature.

2.2.1 Industrial context

A typical paced automotive assembly line is physically divided into more than a hundredconsecutive workstations. The vehicles to produce are placed on a constant speed conveyorbelt (meters/min) which goes through every workstation. Hence, every vehicle to be producedgoes through these serial workstations at the same speed and in the same order. In eachworkstation, one or more operators perform a number of tasks on each vehicle. Time spentby a vehicle in a workstation is called the cycle time. Physically this means that when thejth vehicle in the sequence exits a given workstation the (j+ 1)st vehicle enters it and spendsa time equal to the cycle time in this station (see figure 2.1).

One of the challenges in a paced assembly line is assigning the operations to the worksta-tions such that the workload of each station is the most homogeneous possible. This is calledthe assembly line balancing and is extensively studied in the literature [SC06],[BS06]. Whenbalancing the line, the average time of the tasks to be performed in every workstation shouldnot exceed the cycle time. In practice, for economical and technical reasons, durations of sometype of vehicles are allowed to exceed the cycle time at some workstations. These vehicles

2.2. WHERE DO WE STAND IN THE LITERATURE? 13

overload the related workstation since the operator needs more than the cycle time to com-plete the tasks on the vehicle. We will refer to them as work intensive vehicles. The operatoraccumulates a delay when he performs a task on a work intensive vehicle. If the succeedingvehicles have high operation durations as well, the operator cannot recover his delay. If thetotal delay becomes important and no recovery is possible, either a utility worker is calledup for help or the line is stopped (depending on the company practice). These emergencysolutions are costly and may also degrade the product quality. The car sequencing emergesas a remedy to this problem.

2.2.2 Related car sequencing literature

Car sequencing is an extensively studied discipline in the literature. The interested readerscan refer to [SCNA07] and [BFS09] for a recent and complete overview of the domain. Theobjective of car sequencing is to generate a sequence of vehicles such that the work intensivevehicles are spaced from one another in the sequence to permit the operator to recover hisdelay. We note that this method is not only mentioned in the car sequencing literature butis also applied in practice at the car manufacturers.

Distancing troublesome vehicles in a car sequence is obtained by a set of spacing con-straints. A spacing constraint is defined by two data:

• A criterion which represents an option or a combination of several options. The vehicleshaving the criterion (i.e. the options) corresponds to the work intensive vehicles andare to be spaced.

• A ratio N/P which represents the maximum number (i.e. N) of vehicles with thecriterion in a sliding window of P vehicles in the sequence.

Bolat and Yano [BY92a, BY92b] have shown that if N/P is well chosen and a sequencerespecting N/P is found, total utility work is minimized.

The quality of the car sequence is highly dependent on a correct definition of the spacingconstraints. In practice, the criteria of the spacing constraints are defined based on experienceand the ratio N/P is often deduced by the number of vehicles to produce. In the literature,no study exists on the selection of the criteria of the spacing constraints and very few studieshave been reported on the calculation of N/P (see [VJ06], [YR91], [BY92a], [BY92b]). Amongthese studies, [YR91], [BY92a] and [BY92b] calculate N/P for an assembly line having a singleworkstation and [VJ06] consider that the calculated N/P fits perfectly all workstations of theassembly line. Both assumptions are unrealistic in an industrial context. Therefore, in ([6]),we have proposed a model for the definition of spacing constraints to answer the followingquestions: which options should be spaced (i.e. definition of the criteria) and by whichdistance (i.e. calculation of Nk/Pk, where k is the index of the constraint). In section 2.3.1,we describe this method briefly.

2.2.3 Measuring difficulties to obtain a car sequence

Defining good quality spacing constraints is a necessary but not a sufficient condition to havea smoothly operating assembly line. As a first difficulty, it is not always possible to generatea car sequence which satisfies all spacing constraints.


The quality of a car sequence is usually measured by counting how many times eachconstraint is violated in the sequence as in equation 2.1. This equation is proposed by [Jol05].A similar counter is given by [FB08] and returns the same result.

Ck =

Qtot∑j=1

(xj,k ×min(max(−Nk +∑

l=j−Pk+1

xj,k, 0), 1)) (2.1)

where Qtot is the number of vehicles in the sequence,Nk and Pk are as defined before,xj,k = 1 if the vehicle at position j in the car sequence has the criterion of constraint k,xj,k = 0 otherwise.

Equation 2.1 counts the number of violations only for a single constraint k. To have aglobal quality measure, it is proposed to combine all violations in a weighted sum as given inequation 2.2. Here, wk is the weight of constraint k, K is the set of all spacing constraintsand |K| reflects the cardinality of this set (i.e. the number of constraints).

C =

|K|∑k=1

(wk × Ck) (2.2)

The drawback of this method is that we should first generate the whole sequence, simulateit, and then count the number of violations. This is rather time consuming and provides onlyan analysis a posteriori.

In late nineties, Comby [G.96] proposed a method to evaluate, a priori, the degree ofdifficulty of obtaining a car sequence in respecting a single spacing constraint as in equation2.3. Here, Q is the number of vehicles ordered (and has to be produced) having the optionidentified by the spacing constraint.

Ccomby =

(NP ×Qtot

)−Q

NP ×Qtot

(2.3)

Hence, Ccomby evaluates the difference between the assigned ratio N/P and the rate atwhich the option is to be produced Q/Qtot. If Ccomby is negative, the spacing constraintcannot be respected. If it is positive and close to 0, the spacing constraint should be difficultto respect. And, if it is close to 1, the constraint should be very easy to respect.

Even though this method provides a a priori analysis, the indicator Ccomby is insufficientto identify if a sequence is easy or difficult to obtain. First of all, it evaluates a single spacingconstraint at a time (i.e. a single set of options) and ignores the possible interaction betweenconstraints (i.e. caused by vehicles requiring two or more sets of options at the same time).We note that in an industrial context, the number of spacing constraints is in the order of10 to 20. Secondly, Ccomby doesn’t differentiate between two ratios if one is a multiple of theother. For instance, it will return the same value for 1/2 and 2/4. In reality, the ratio 1/2is more restrictive than ratio 2/4 since the latter ratio authorizes more combinations of carsequences than the former one.

We have proposed a solution to evaluate the difficulty to generate a car sequence whichtakes into account the possible interactions which may exist among the spacing constraints.This method is backed up by a computerized tool which is currently used by our industrialpartner. We will briefly describe the method in section 2.3.2.

2.3. SOLUTION METHODS 15

2.2.4 Evaluating the flexibility

As we have seen in section 2.2.3, if the number of a work intensive vehicle to produce is greaterthan the number induced by its constraint N/P , the generated car sequence will violate theconstraint from time to time. And when the constraint is violated, we expect to see delaysaccumulating at the workstations. Therefore, utility workers are employed bringing a form offlexibility on the assembly line.

In the case where the number of a type of work intensive vehicle to produce is less thanthe number induced by its constraint N/P , we will have another point of view: Since thecapacity of the assembly line is fixed, if we produce more vehicles with this option then wewill limit the quantity of other options. Hence, the available flexibility for other options willbe influenced.

These two arguments explain in a sketchy way the relation between a car sequence andthe flexibility on an assembly line.

The scientific literature on flexibility is abundant. Numerous state-of-the-art have beenreported in the domain of production systems and supply chain management. [AS90, DTT98,DF00, BAD+00, MM07] are some to name. Furthermore the term flexibility may have dif-ferent definitions depending on the context. Therefore, here we will recall the definition ofthe flexibility as treated in our research and only mention some articles which are directlyrelated to our work. In our work, the flexibility of the assembly line is defined as the capacityof the production system to overcome difficulties related to the forecast errors, occurrence ofunexpected events and the difficulties related to car sequencing. In this definition, we observetwo types of flexibility previously defined in the literature [AS90]: (i) volume flexibility whichis defined as the capacity of the production system to accept a variable production volume inorder to satisfy the variable customer demand and (ii) product-mix flexibility which is definedas the capacity of the system to modify the product-mix required on a given planning horizon.The methods we developed to evaluate the flexibility of the assembly line is expressed in termsof volume flexibility. The product-mix flexibility is indirectly exploited since the car sequencewill constrain the production volume during a time window as discussed at the beginning ofthis section.

The methods to measure the flexibility is as divers as its definition. There exists differenttechniques, quantitative or qualitative, depending on type of flexibility or expressed as anagregate [BAD+00]. [Gup93] suggest that the measuring method of the flexibility shouldcomply with its definition and context. As we are interested in volume flexibility, we focusedour search in this domain. The sources and hence the related measuring tools in flexibility arenumerous. The major tools are listed as the set-up time reduction methods, advanced pro-duction technologies, polyvalency of operators and extra production capacity [MJ09]. Basedon our definition of flexibility, our measurement method is oriented towards evaluting theextra production capacity put in place during the dimensioning of the assembly line. Thismethod is presented briefly in section 2.3.3.

2.3 Solution Methods

In this section, we will present the methods developped for each one of the line of actionsdetailed above.


2.3.1 Defining spacing constraints for car sequencing

We recall that the objective of a spacing constraint is to smoothen the workload of theoperators to avoid calling up utility workers or stopping the line. Therefore, we believe thatthe quality of a spacing constraint shall be observed on the assembly line. If one is ableto construct a car sequence which respects the spacing constraints, then he shall expect tosee very few workload problems on the assembly line. Otherwise the initial objective is notatteined. During the thesis of Aymeric Lesert [Les06], we had the chance to observe theassembly line at PSA Peugeot-Citroen. We were quite surprised to see that even though nospacing constraints were violated for some sequences (i.e. equation 2.2 returns 0), the utilityworkers were frequently summoned. Furhermore, for the sequences where spacing constraintsare violated (i.e. equation 2.2 returns a positive value) the vehicles which were violating thespacing constraint were not always those which were actually creating a workload on theassembly line. From these observations, our conclusion was that the spacing constraints werepoorly defined.

Therefore, we proposed models which are based on the observation of the movements ofan operator, to capture how he accumulates delay. The idea is to incorporate this observationin the definition of spacing constraints.

Movements of an operator in his workspace

In figure 2.2 the movements of an operator is sketched. As can be seen, in practice, theoperator may utilize a space greater than the length of his workstation. The real boundariesof workstation i is hence delimited by a lower limit Mini, and an upper limit Maxi as shownin figure 2.2. These limits are calculated based on the technical constraints of tools usedin the workstation and the specificities of the neighboring stations (i.e. the operator shouldnot bother the neighbor operators). By definition Mini ≤ 0 and Maxi ≥ Tcycle. The termworkspace is used to refer to these practical boundaries of a workstation. The movementsof the operator are illustrated by bars and arrows in figure 2.2: the operator escorts eachvehicle during the assembly task (illustrated by horizontal bars) and then walks back to treatthe next vehicle (illustrated by arrows). If the operator is ahead of his time, he may startworking on a vehicle before it actually enters the workstation (case of vehicle 2 in figure 2.2).Similarly, if he has accumulated some delay, he may continue working on a vehicle beyond hisown workstation (case of vehicles 2 and 3 in figure 2.2). When an operator can not completeall assembly tasks within the workspace (case of vehicle 4 in figure 2), one of the followingsolutions can be used:

1. The operator can stop the line to finish his job and restart the line afterwards (Toyota’sANDON method) (see [Tsa67], [KPC01]).

2. A utility worker can be called up to complete the unfinished work when the vehicle exitsthe workspace (see [VJ06], [BY92a], [BY92a]).

3. A utility worker treats a complete vehicle which cannot be completed in the workspace(see [VJ06]).

We used the third approach since it satisfies the quality requirements of PSA Peugeot-Citroen. Based on this hypothesis, the vehicle 4 in figure 2.2 will completely be treated by autility worker as soon as it enters the workspace. The operator can then recover his delay byskipping the 4th vehicle and handles the next one in the sequence.


Figure 2.2: Movements of an operator in his workspace

Methods to minimize the number of calls for utility workers

In collaboration with Olivier Briant and Radwan El-Hadj-Khalaf, we proposed a Mixed In-teger Linear Programming (MILP) approach to directly minimize the number of times theutility workers are called for ([EHK06, 1]) as presented below. We use the following notations:

Tcycle: cycle timeI: number of workstationsN : number of vehiclesMini: upstream limit of workstation iMaxi: downstream limit ofworkstation itik: processing time of vehicle k at workstation iDij : starting time of operations on jth vehicle at workstation iFij : finishing time of operations on jth vehicle at workstation iTij : processing time of work on the jth position at workstation i

The decision variables are;

Xkj =

{1 if vehicle k is assigned to the jth position in the sequence0 otherwise

Yij =

1 if jth vehicle on workstation i requires help of a utility worker,

i.e. Fij > Maxi0 otherwise

Note that Tij = Xkj .tik , Fij = Dij + Tij = Dij +Xkj .tik and

Dij =

0 if j ≤ nimax(Mini, Dij − Tcycle) if Fij ≥Maximax(Mini, Fij − Tcycle) otherwise

The MILP is then expressed as follows:


Minimize Z =I∑

i=1

N∑j=1

Yij

subject to:

Dij = 0 ∀j = 1 ∀i = 1..I

Dij ≥Mini ∀j > 1 ∀i = 1..I

Dij ≥ Dij − Tcycle −M.(1− Yij) ∀j > 1 ∀i = 1..I

Dij ≥ Dij +N∑k=1

Xkj .tik − Tcycle −M.Yij ∀j > 1 ∀i = 1..I

N∑k=1

Xkj = 1 ∀j = 1..N

N∑j=1

Xkj = 1 ∀k = 1..N

M.Yij > Dij + (N∑k=1

Xkj .tik)−Maxi ∀j = 1..N ∀i

Yij , Xkj ∈ {0, 1} ∀k, j = 1..N ∀i

The first set of constraints initializes Dij , the second indicates that operator can not startworking on a vehicle before the vehicle enters the boundaries of the workstation. Third andfourth sets of constraints express the calculation of Dij in a linear manner. The fifth setof constraint translates that only one vehicle can be assigned to a given position j in thesequence. The sixth set of constraints assures that a given vehicle k is assigned to a uniqueposition j. Finally, the last set of constraints is used to force Yij to be equal to 1 when theestimated finishing time of the jth vehicle on the ith workstation exceeds Maxi.

Using this MILP, we can obtain optimal solutions for small instances. In order to solveindustrial size problems (i.e about 1000 vehicles and 200 workstations), we proposed a heuris-tic based on simulated annealing. This heuristic is compared to other heuristics which arebased on the movements of an operator in his workspace (see [Tho67], [Tsa67], [KPC01]). Weused real data from Poissy and Sevel Nord production sites for comparison purposes. Thisalso provided us a chance to have a comparison with the sequence obtained using company’sscheduling tools. Our heuristic outperforms the previously published heuristics (comparisonmade after 1 minutes execution). For instance, depending on the data set the number oftimes the utility workers are invoked may be 11,08 to 396,31 % more in the case of [Tho67],19,82 to 94,04 % more for the best solutions found by [KPC01] and 14,04 to 47,12 % morefor the best solutions obtained by [Tsa67]. We note that, in the case of [Tsa67] solutions areobtained in 1 hour on the average, while the heuristic proposed by [Tho67] and [KPC01] take2 to 5 seconds on the average.

The proposed solution outperforms the company’s current scheduling tool as well in termsof the number of invoked utility workers1. However the implementation on the informationsystem is not straightforward: First of all, the minimization of the utility workers is not theonly objective to consider. Secondly, right now, all criteria are expressed and are injectedinto the company’s scheduling tools as a set of spacing constraints. The potential gain ofimplementing the above mentioned method as is is not considered significant compared to

1No performance data will be given due to confidentiality


Figure 2.3: Representation of the workload of an operator at his workstation

the costs it is expected to generate to modify the existing information systems. Therefore, wehave opted for converting the idea of movements of an operator into an expression describedby a set of spacing constraints.

Defining the criterion of a spacing constraint

In ([6]), we have proposed a method for defining the ratio constraints (i.e. the criterion andthe ratio) such that the workload of the operators can be taken into account properly by thespacing constraints.

The workload of an operator in his workstation can be represented as an ordered set ofjobs as shown in figure 2.3. All vehicles j to be produced in the workstation i are sorted inascending order of Ti,j . The vehicles having the same Ti,j are grouped together and representedgraphically. In the example workstation of figure 2.3 there are 5 different operation times: 2groups of vehicles with Ti,j ≤ Tcycle (in light grey) and 3 groups of vehicles with Ti,j > Tcycle(in dark grey). These latter groups correspond to the work intensive vehicles.

In order to quantify the capability of a criterion to represent the work intensive vehiclesof workstation i , we need to compare two subsets of vehicles assembled in this workstation.Let, V be the set of all vehicles to be produced on the assembly line. We define the followingsets of vehicles:

Vk = {j ∈ V |j has the criterion of spacing constraint k} (2.4)

Vhi= {j ∈ V |Ti,j > Tcycle} (2.5)

where and Vk 6= ∅ and Vhi6= ∅. Indeed, a workstation with Vhi

6= ∅ can accept anysequence of vehicles without causing delays for the operator. Similarly Vk 6= ∅ means thatthere are no vehicles to space. Table 2.1 summarizes 5 different relationships which existbetween a workstation i and a spacing constraint k. Depending on these relationships, aspacing constraint will be more or less efficient to handle work overloads generated in aworkstation. We note that in the literature, only the first type of relationship is considered[VJ06].

The first and last relationship describe the extreme cases. In the first relation ship, allwork intensive vehicles are perfectly identified and covered by the spacing constraint, whereasthe final relationship says that the criterion chosen for the spacing constraint fails to identifythe work intensive vehicles.

To evaluate the level of pertinence of a spacing constraint k with respect to a workstationi, we propose the impact factor IFi,k given in equation 2.6.


Condition Relationship

Vk = VhiWS is constrained by a SC

Vk ∩ Vhi= Vhi

and Vk − Vhi6= ∅ WS is over-constrained by a SC

Vk ∩ Vhi= Vk and Vhi

− Vk 6= ∅ WS is under-constrained by a SC

Vk ∩ Vhi6= ∅, Vk ∩ Vhi

6= Vk and Vk ∩ Vhi6= Vhi

WS is impacted by a SC

Vk ∩ Vhi= ∅ WS is not impacted by a SC

Table 2.1: Relationship between a workstation (WS) and a spacing constraint (SC)

IFi,k =|Vk ∩ Vhi

||Vhi|× |Vk ∩ Vhi

||Vhi|

(2.6)

where Y and |Y | is the compliment and cardinality of the set Y , respectively. We note that0 ≤ IFi,k ≤ 1. IFi,k = 1 (respectively, IFi,k = 0) refers to a workstation i constrained(respectively, not impacted) by a spacing constraint k. Hence the higher the IFi,k, the betterthe work intensive vehicles are identified for this workstation. Hence, the options (or acombination of options) with the highest IFi,k shall be chosen as the criterion of the spacingconstraint. In ([6]) we have provided numerous experiments which show that the impactfactor IFi,k and the number of times the utility workers are called up are highly correlatedand IFi,k can be used to identify troublesome vehicles.

We recall that the number of spacing constraints are in the order of 10 to 20 and the num-ber of workstations may go up to several hundreds. Equations 2.7 and 2.8 give, respectively,a global evaluation of the pertinence of set of all constraints or a workstation i and for theglobal assembly line.

IFi = maxk∈K

(IFi,k) (2.7)

IF =

∑i∈Wh

IFi

|Wh|(2.8)

where Wh is the set of workstations with at least 1 work intensive vehicle to treat.

IFi can be used to supervise the assembly line. By ordering the workstations by ascendingor descending order of IFi, the person in charge of the assembly line balancing can easilyidentify the workstations which will need a particular supervision. Indeed, the weaker theIFi is for a workstation, the less efficient is a set of spacing constraints to identify the workintensive vehicles at this workstation. And IF can be used to compare different sets of spacingconstraints or to evaluate the impact of exchanging a constraint with another one.

Defining the ratio of a spacing constraint

In practice, N and P which define the ratio of a spacing constraint are often deduced fromthe number of vehicles having the criterion and the total number of vehicles to produce,respectively. The workload of operators are completely ignored in this case.

In the literature [YR91, BY92a, BY92b] and more recently [VJ06] compute N/P consid-ering the information on the duration of the assembly tasks and the workload of an operator.


Figure 2.4: Simplification of the processing times of a workstation

[BY92a] propose a regenerative sequencing procedure. According to their method, a maxi-mum number of consecutive vehicles with Ti,j > Tcycle which a workstation can accept withoutrequiring a utility worker is scheduled first (i.e. N). Then a maximum number of vehicleshaving Ti,j ≤ Tcycle are assigned consecutively to recover the delay (i.e. P −N). Repeatingthis pattern will regenerate the sequence when the operator becomes idle either because hereturns naturally back to his initial position after the treatment of a vehicle or a utility workeris called up at one point of the sequence. In [BY92a] the length of the work station is givenin terms of number of jobs. Below we give the notations in terms of temporizations. Never-theless, the calculation of the ratio remains the same. We note that, each notation is definedfor a workstation i. We dropped the index i from the notation for the sake of simplicity.

Another simplification is on the duration of the tasks on a workstation. The workstationsexperience diverse Ti,j values due to the diversity of tasks to be performed at each workstation.Considering each Ti,j separately for the calculation of N/P will tremendously increase thecomputational complexity. To overcome this difficulty, [VJ06] propose a method to simplifythe representation of a workstation with various task durations into a workstation with twotemporizations (see figure 2.4):

• A unique duration of assembly tasks, Tsup, is assigned to all vehicles with Ti,j > Tcycle.

• A unique duration of assembly tasks,Tinf , is assigned to all vehicles with Ti,j ≤ Tcycle.

We note that in figure 2.4, Tsup and Tinf are shown only for a worst case scenario. Thecalculations can be made for average or best case scenarios, as well.

In figure 2.4, Rsup corresponds to the supplementary delay that a vehicle j ∈ Vhiadds up

to the workload of the operator. And Rinf gives the reduction in delay obtained by assigninga vehicle j ∈ Vhi

in the car sequence. Let Rmax be the maximum delay that an operator canaccumulate. Rsup, Rinf and Rmax are calculated as follows and N and P are calculated as inequation 2.9.

Rinf = Tcycle − TinfRsup = Tsup − TcycleRmax = (Max−Min)− Tcycle


N =

⌊Rmax

Rsup

⌋and P = N +

⌈N ×Rsup

Rinf

⌉(2.9)

Once a sequence is generated using a spacing constraint, this same sequence of vehicleswill go through all workstations. Hence, calculating N/P based on only the temporization ofa single work station is not justified. We propose the following procedure which helps us todefine a unique N/P which is convenient for all workstations. To this end, we introduce thenotion of compatibility of ratios.

Definition 1. A ratio N ′/P ′ is compatible with a ratio N/P if all possible combinations ofN ′ work intensive vehicles in a window of P ′ vehicles respect the ratio N/P . Formally, a ratioN ′/P ′ is compatible with a ratio N/P if the inequality given in equation 2.10 is respected.

⌊P

P ′

⌋×N ′ + min(P − P ′ ×

⌊P

P ′

⌋, N ′) ≤ N (2.10)

For instance, the ratio 1/2 is compatible with the ratio 2/4 because every sequence ofvehicles respecting the ratio 1/2, respects the ratio 2/4 as well. On the other hand, the ratio2/4 is not compatible with the ratio 1/2. Let’s take the sequence XXOOXXOO... (where Xis a vehicle with the criterion of the constraint). The Ratio 2/4 is respected in this sequence,while the ratio 1/2 is violated.

Definition 2. For a given constraint k with the ratio Nk/Pk we can define the set of com-patible ratios, RNk/Pk

, using equation 2.11. We note that RNk/Pkis an infinite set.

RNk/Pk= {N ′k/P ′k|

⌊Pk

P ′k

⌋×N ′k + min(Pk − P ′k ×

⌊Pk

P ′k

⌋, N ′k) ≤ Nk,

∀(N ′k, P ′k) ∈ N2, 0 < N ′k < P ′k}. (2.11)

Definition 3. For each workstation i constrained by the spacing constraint k, a set of commonratios can be calculated as in equation 2.12. Here, Wk corresponds to the set of all workstationswhich are constrained by k.

Commonk =⋂

i∈Wk

RNi/Pi(2.12)

Using the above definitions, we can find a unique ratio Nk/Pk for a given ratio k whichapplies to all workstations in the assembly line.

As the final step of algorithm 1 indicate, we can choose any ratio in the Nk/Pk. Mem-bership to Commonk guarantees that the ratio will be acceptable for every workstation. Inthis case, if we would like to produce a maximum number of vehicle having the criterion, thenatural choice will be to opt for the maximum Nk/Pk value in the set. Or, one may choose aratio which guarantees to produce the quantity required by the production plan, and so forth.


Algorithm 1 : Calculating a common ratio for a spacing constraint

1: For each workstation i constrained by spacing constraint k, calculate the ratio Nk/Pk

using equation 2.9. This will result in different values for Nk/Pk.2: For every different possible Nk/Pk calculated in step 1, build the set of the compatible

ratios RNk/Pkusing equation 2.11

3: Find the set of common compatible ratios, Commonk using equation 2.12. These ratiosare common and applicable to every workstation.

4: Choose one of the ratios from Commonk as Nk/Pk.

Experimental results on the spacing constraints

We have conducted various numerical experiments, both using generated and real industrialdata. Through these experiments, we have tested how well the impact factor, IF , of aset of constraints is capable of capturing the need for utility workers. The performance ofspacing constraints defined using the above method is also compared to the spacing constraintsapplied at the company at the time of the experiments. These numerical results, as well as adescription of the experimental setting for generated data can be found in ([6]). We will givehere the major observations drawn from these numerical tests:

• As IF approaches to 1, the number of times the utility workers are called up decreases.Furthermore, most of the emergency calls are identified by a violation of the spacingconstraint in the car sequence.

• Some emergency calls may still not be identified by the violation of a spacing constraint.Additional experiments have shown that this is due to the simplifying assumption madeon the temporizations of tasks on a workstation.

• The experiments on real data from Sevel Nord assembly line have revealed some inter-esting results as well.

– The 11 spacing constraints used at the production site at the time of the experi-ments had an IF = 0.57. The company’s sequencing tool was able to generate a carsequence with zero violation of the constraints, C = 0. However, 176 emergencycalls were recorded during the duration of the study.

– Using our method we constructed 11 new spacing constraints with IF = 0.98.The sequencing tool was able to generate a sequence with 66 violations (in theworst case scenario for temporization simplifications). Simulating the sequencehas pointed out that 64 out them were corresponding to an emergency call on theassembly line.

– The difficulty to generate a car sequence with C = 0 using our spacing constraintshas also pointed out that some of the workstations were poorly dimensioned.

– Finally a sensitivity analysis on the number of spacing constraints to apply hasshown that 15 spacing constraints are enough to cover the whole assembly line.As we increase the number of constraints (above 15), obtaining a car sequencebecomes difficult. Furthermore, the number of emergency calls for utility workersreaches to a stability. Hence some of the violations of the spacing constraints resultfrom the difficulty of generating a sequence and do not reflect an emergency onthe assembly line.


2.3.2 Identifying difficulties to generate a car sequence

As we have seen in the previous section, as the number of spacing constraints increases,obtaining a car sequence with zero violation gets more difficult. One of the reasons is thatsome vehicles may carry more than one option. And each one of these options may characterizea different spacing constraint. This creates a dependence between the underlying spacingconstraints. Each one of the constraints may be easy to satisfy when considered individually.However, obtaining a sequence may be troublesome when they are considered together. Wename this phenomenon as the interactions of spacing constraints. The following exampleillustrates the phenomenon:

We consider a production plan where each vehicle can have zero, one or two options(options are called A and B). We have QA (respectively QB) vehicles requiring the optionA (respectively B). We have four different types of vehicles (Type ∅ , Type A, Type B andType AB) with quantities qo, qA, qB and qAB respectively. Type X corresponds to vehiclesrequiring option X. The total quantity, Qtot, corresponds to the total number of vehiclesto produce, as before. Assume that a spacing constraint is defined for each option with theratios NA/NB = 1/3 and NA/NB = 1/2. Let Qtot = 18, qA = 6, qB = 9. As seen in figure2.5, obtaining a sequence depends on qAB required by the production plan.

A A A A A A

B B B B B B B B

A A A A A A

B B B B B B B B

A A A A A A

B B B B B B B B

Case 1:

Case 2:

Case 3:

qAB = 0

qAB = 5

qAB = 3

B

B

B

Figure 2.5: An illustration of interactions of constraints

In the case 1 of figure 2.5 the production plan asks for qAB = 0. The sequence shouldrespect constraint A (line 1 of the figure) and constraint B (line 2 of the figure). The completesequence is the superposition of these two sub-sequences: vehicle 1 is of type A, vehicle 2 isof type B, 3 is of type ∅, 4 is of type AB, and so forth. Case 1 illustrates that it is impossibleto find a sequence respecting both constraints simultaneously if qAB = 0 in the productionplan. For a vehicle having both options, shifting an option A (on the left or on the right) inthe sequence will create a violation of constraint A. Similarly, case 2 shows that if qAB = 5then too many vehicles require both options. Therefore, some of the single options should beshifted in the sequence to create vehicles having both options. But any such move will createa violation of the constraint A. Finally, if qAB = 3 as in case 3, it is easy to find a sequence


satisfying both spacing constraints simultaneously.

Counting the number of violations as in equation 2.2 is an a posteriori way of knowing if acar sequence respecting the constraints is easy or difficult to obtain. The counter proposed byComby given in equation 2.3 provides a a priory analysis but only for a single constraint. Forthe above example, Ccomby is equal to 0 for both spacing constraints A and B in all 3 cases.It is hence expected to be difficult to find a sequence which respects constraints A and Bindividually. However, figure 2.5 illustrates very well that this difficulty is highly conditionedby the number of vehicles having both options.

We have shown in ([5]) that under certain conditions, we can calculate a range of qAB

values which guarantee that generating a car sequence satisfying both constraints is easy. Fortwo spacing constraints A and B with ratios 1/PA and 1/PB, we state 3 properties which givethe conditions to have no violations in a sequence (i.e. C = 0). The proofs of these propertiesare given in [Les06].

Property 1: If PA and PB are multiples of each other then there exists a sequencesimultaneously respecting the constraints with 1/PA and 1/PB for all qAB.

Property 2: If PA and PB are prime and if qAB =⌊

Qtot

SCM(PA,PB)

⌋or qAB =

⌈Qtot

SCM(PA,PB)

⌉then there exists a sequence simultaneously respecting the constraints with 1/PA and1/PB. Here SCM(PA, PB) is the smallest common multiplier of PA and PB.

Property 3: If PA and PB are neither multiples nor prime and if qAB ≤⌈

Qtot

SCM(PA,PB)

⌉,

then there exists a sequence simultaneously respecting both constraints with 1/PA and1/PB.

In our industrial context, about 90% of the spacing constraints were of type 1/P . Theabove properties were hence applicable for the majority of the constraint couples.

In the general case where NA > 1 and NB > 1, we proposed an algorithm which calculatesnumerically two limits Q+ and Q−. If Q− ≤ qAB ≤ Q+, it is easy to construct a carsequence which respects both constraints simultaneously. We note that, this latter statementis presented as a conjecture. We do not have a formal proof. However, all our numericalexperiments back up its validity. In ([2]), we proposed a computerized decision supporttool which helps operation managers to choose an appropriate set of ratios for the spacingconstraints to minimize the risk of interaction among couples of constraints. This decisionsupport tool makes use of the above properties and the numerical method proposed for thegeneral case. A screen shot of the tool is given in figure 2.6. A positive number in the matrixsignals an interaction.

In this screen shot, we observe that the spacing constraints HRPC1 and HRPC2 areinteracting. HRPC1 is currently assigned a ratio of 1/4 while HRPC2 has a ratio of 1/11.The scheduler may choose to modify one or both ratios. To this end, a set of ratios areproposed to the scheduler from the set of common compatible ratios (see definition 3). Everytime, a new ratio is entered, the decision support tool recalculates the interactions. Thescheduler modifies ratios until all elements of the matrix is equal to zero.

This tool helps the scheduler to affine the ratios of the spacing constraints so as to reducethe number of violations in the car sequence. From an industrial point of view, this toolhelps assigning appropriate flexibility on the assembly line. We note that, even though noformal model of interactions among spacing constraints existed, the line managers in PSA


Figure 2.6: A decision support tool to reduce interactions of constraints

Peugeot-Citroen were aware that some combinations of options create difficulties. To avoidthese difficulties, they used to install extra resources (i.e. flexibility) on the assembly line.With our decision support tool, it is now possible to adjust this flexibility closer to its realrequired value. This tool is currently in use in PSA Peugeot-Citroen.

2.3.3 Measuring the flexibility of the assembly line

The final goal of our studies were to evaluate the flexibility available on the assembly line.In ([7]), we proposed a method which measures the level of flexibility of the work stationsin a mixed model assembly line. The method is based on measuring the difference betweenthe maximum production capacity installed to produce the work intensive vehicles and theproduction capacity actually available due to line balancing. This is a volume flexibility.For measuring this volume flexibility, we have inspired from an existing measure proposed by[PW99]. This measure, denoted F is based on comparing two limits: the maximum productioncapacity Capmax and a limit for profitability Lp (equation 2.13).

F =Capmax − Lp

Capmax(2.13)

In our case Capmax corresponds to the maximum number of work intensive vehicles whichcan be produced without additional resources such as the use of utility workers, and Lp cor-responds to the number of such vehicles expected to be produced based on demand forecasts.Equation 2.13 gives a generic measure for volume flexibility but in practice the calculation ofCapmax is not obvious. Capmax can be calculated in a static or a dynamic manner. In theassembly line context, the maximum static production capacity, Capstamax, is calculated withrespect to a given balancing of the line. Hence, it only reflects the fact that a theoretical meanworkload limit for the workers are not exceeded (see equation 2.14). A maximum dynamic

production capacity, Capdynmax on the other hand, takes into account instantenous workloadincreases due to the car sequence and heavily relies on the definition of spacing constraints(see equation 2.15):


Kobj ≥Qtot × Tcycle

(Qtot − Capstamax)× Tinf + Capstamax × Tsup

⇐⇒ Capstamax =

Qtot ×Tcycle

Kobj− Tinf

Tsup − Tinf

(2.14)

Capdynmax =

⌊N

P×Qtot

⌋(2.15)

Equations 2.14 and 2.15 are defined for a given workstation. The ratio N/P in equation2.15 is calculated as described in the previous section. Capstamax is a physical limit and alwaysreturns a value greater than or equal to Capdynmax. The operations managers have a tendencyto evaluate the available capacity by Capstamax. We note that the flexibility which is reallyavailable depends on Capdynmax. Comparing Capdynmax with the forecasted demand (or sometarget flexibility level) will give how much flexibility is actually available on the assemblyline.

We have tested this idea on the production sites of Peugeot-Citroen. In our industrialcontext, there were 2 target flexibility values:

• Market flexibility:(denoted Fm) refers to the flexibility that the production line canoffer to marketing department. Such a flexibility permits accepting changes in thecustomer portfolio to a certain extent. For instance, the forecasted demand for vehicleswith sun roofs could be 100 but the assembly line is dimensioned to produce 120. Hence,marketing department can accept 20 more customer demands if an increase in demandoccurs during the production period. In short, this flexibility accounts for forecasterrors. Fm is negotiated with the marketing department and the assembly line manageris bound to install the negotiated flexibility based on their agreement.

• Production flexibility: (denoted Fp) refers to the flexibility installed to overcome anyoperational difficulties. In our case, this flexibility accounts for occurrence of unexpectedevents and difficulties related to scheduling, and is installed on top of market flexibility.If we take the same example, the assembly manager may choose to dimension theassembly line to produce 135 sun roofed vehicles. This means that any unexpectedevent shall be resolved during the production time of at most 15 sun roofed vehicles.

These flexibilities are installed during the line balancing and resource allocation processes.What interest the operations managers is to know if the real available flexibility on theassembly line matches with the target values or not. We proposed to measure 3 levels offlexibilities:

• Total available flexibility, ∆(Ftot) = Capdynmax−F∅Capdynmax

.

• Additional available flexibility for marketing, ∆(Fm) = Capdynmax−Fm

Capdynmax.

• Additional available flexibility for production, ∆(Fp) =Capdynmax−Fp

Capdynmax.


Unexpected events

Scheduling

Forecast errors

Forecasted

Demand

of work intensive

vehicles

Capstamax

Capdynmax

Qtot

∆(Fp)

∆(Fm)

∆(Ftot)

Fp

Fm

F∅

Figure 2.7: Measures of flexibility

Figure 2.7 illustrates target production values including flexibilities, Fp and Fm, as wellas the real available flexibilities ∆(Fp), ∆(Fm) and ∆(Ftot). Here, F∅ means that the line isdimensioned based on forecasted values and no flexibility is installed.

∆(Fp), ∆(Fm) and ∆(Ftot) helps us to identify if the work stations are correctly dimen-sioned or not. In ([7]), we proposed to classify the workstations based on their availableflexibilities:

• If ∆(Ftot) ≤ 0 then the workstation is ill-dimensioned.

• If ∆(Ftot) > 0 and ∆(Fm) < 0, we classified this workstation under the category insuf-ficiently dimensioned. Since the contract with marketing department is not respected.

• If ∆(Fm) > 0 and ∆(Fp) < 0 then the workstation is in a risk group (in case ofunexpected events). Here, the level of risk depends on the amplitude of ∆(Fp)

• If ∆(Fp) > 0, then the workstation is considered over-dimensioned (this is the case infigure 2.7).

We have applied these flexibility measures at our industrial partner on a daily productiondata. The assembly line is composed of 194 workstations and during the experiments the lineexperienced a total of 1119 emergency calls which are handled by the utility workers. Ouranalysis returned the results summarized in table 2.2. Further experimental results can befound in ([7]).

Our method helps identifying ill-dimensioned workstations. As we see in table 2.2, thepercentage of emergency calls validates that these workstations are also the troublesome ones,operationally speaking.

2.4. MAJOR CONTRIBUTIONS 29

Class % workstation % emergency calls

Ill 35 93

Insufficient 8.8 2.90

Risk 4.6 0.6

Over 51.6 3.4

Table 2.2: Classification of workstations

2.4 Major Contributions

In this domain, we have several contributions in various domains related to the very complexcontext of automobile industry.

We believe that the most important contribution is in the domain of car sequencing viathe proposition of a method which helps defining different elements of a spacing constraint:the criterion and the ratio. We recall that the definition of a spacing constraint has not beenstudied until now: When generating the car sequence, the spacing constraints are assumedto be known and the sequencing tools take them as an input data. In our studies, we haveshown that to have a good quality car sequence, it is not sufficient to have high performancescheduling tools which guarantee that no spacing constraints are violated. As long as thespacing constraints are poorly defined, the generated car sequence fails to capture the realemergency situations that may arise on the production site. In this sense, we believe thatthis research work underlines and fills in a fundamental gap in car sequencing literature.

A second contribution is on the interactions among spacing constraints. This phenomenonwas observed by the operations managers but no rigorous studies were carried out. As wehave seen, when assigning ratios N/P on spacing constraints we may have some flexibilityof choice represented by a set of compatible ratios. We have proposed a computerized toolwhich helps the operations manager to choose the appropriate N/P values to avoid any two-by-two interactions among spacing constraints. Diminishing interactions eases the sequencingprocess. The number of times the constraints are violated is hence decreased, which in turneases the workload of the assembly line operators. We note that this phenomenon is nottypical to automobile industry but is observable in any industrial context which operatesusing a mixed-model assembly line. The method is quite simple to implement especiallywhen the ratios are defined as 1/P .

Finally, we proposed a set of measures in terms of volume flexibility which helps identifyingif the target flexibility levels are attained or not for each workstation. Here, the genericmeasure was initially proposed by [PW99]. Our contribution is in the interpretation of thismeasure especially for the calculation of the maximum production capacity, which is usuallyconsidered from a static point of view. We calculate a dynamic maximum capacity. Hencethe product-mix flexibility can be considered in measuring the available volume flexibility.

At last, but not the least, our methods provide valuable contributions for the practition-ers since we propose new tools for the operations manager to define efficiently the spacingconstraints to respect or to evaluate the performance of the existing ones. These tools arecurrently implemented and used with success at the PSA Peugeot Citroen. The first industrialimpact is a smoother planning of the utility workers. Since the occurrences of emergenciescan be estimated more accurately, a more precise planning of utility workers can be done.This in turn, has a positive impact on the quality of work done as well as a more efficient


utilization of the pool of utility workers.

Bibliography

[1] Radwan El-Hadj-Khalaf, Gulgun Alpan, Olivier Briant, and Aymeric Lesert. Ordonnance-ment des vehicules sur une ligne de montage pour minimiser le nombre d’intervention desmoniteurs. In livre des resumes, Francoro V /Roadef 2007, pages 185–186, Grenoble,France, 2007.

[2] Aymeric Lesert, Gulgun Alpan, Yannick Frein, and Stephane Noire. Outil daide a lanalysedes interactions de contraintes pour lordonnancement dune ligne de montage. In 6emeConference Francophone de Modelisation et simulation, MOSIM06, pages CD–ROM, Ra-bat, Maroc, april 2006.

[3] Aymeric Lesert, Gulgun Alpan, Yannick Frein, and Stephane Noire. Sur le choix descontraintes despacement pour lordonnancement des vehicules dans une usine terminale.In 7eme Congres international de genie industriel, CIGI07, Trois-rivieres, Quebec, Juin2007.

[4] Aymeric Lesert, Gulgun Alpan, Yannick Frein, and Stephane Noire. Classification despostes de travail selon leur flexibilite dans une usine terminale automobile. In 7emeConference Francophone de Modelisation et simulation, MOSIM, page 10 pages, Paris,France, april 2008.

[5] Aymeric Lesert, Gulgun Alpan, Yannick Frein, Stephane Noire, and Francois-RegisVienot. Influence des interactions de contraintes sur l ordonnancement d’une ligne demontage - le cas de deux contraintes. In 6eme Congres international de genie industriel,CIGI05, page 8 pages, Besancon, France, 2005.

[6] Aymeric Lesert, Gulgun Alpan, Yannick Frein, and Stephane Noir. Definition of ratioconstraints for the car sequencing problem. International Journal of Production Research,page doi: 10.1080/00207540903469043, 2010.

[7] Aymeric Lesert, Gulgun Alpan, Yannick Frein, and Stephane Noir. Evaluation de la flexi-bilite des postes de travail dans une usine terminale automobile. Logistique et Management,18(1):57–68, 2010.

31

32 BIBLIOGRAPHY

Chapter 3

Cross docking: a lean solution fordistribution problems

In this chapter, I will present our research work on cross docking problems realized in col-laboration with Bernard Penz, Pierre Baptiste, Rim Larbi [Lar08], Sylvain Bauchau [Bau07],Marie-Laure Espinouse and Onur Ozturk [O08], Jean-Philippe Gayon and Asma Talhi [Tal09].Related scientific production are as follows ([5, 3, 2, 6, 4, 1, 7]).

3.1 Research objectives and our place in the literature

In the literature on cross docking, we can find studies dealing with problems either on thestrategical or the operational level. The solutions for strategical problems often require aninvestment and the decisions taken are not frequently modifiable. For instance, cross docknetwork design (see [DJRZ98], [RVZ98], [CGLR06]) or the layout of cross docking platforms(see [BG04], [BG02], [Gue99]) make part of this category of problems. The articles wemention above are only a few examples. This category of problems are extensively studied inthe literature.

The problems which are handled in the operational level are mainly on the real-timecontrol of the cross docking platforms and hence the decisions are modified on a real-timebasis. Our studies make part of this category of problems.

As mentioned in the introductory chapter, cross docking helps removing unnecessary tasksin the logistics processes. This leaning process is possible if the operations in the platform iswell-orchestrated. The works which will be presented here, have the objective of schedulinginternal operations in a cross dock so that the operational costs are minimized. Several typesof problems are identified on the operational level:

Dock assignment problems: The objective is to find an optimal assignment of in-bound and outbound trucks on the docks. Different optimization criteria are studied.The most popular criterion is the minimization of the weighted sum of the distancetravelled by the products (see for instance, [TC90], [TC92]). Minimization of conges-tion within the cross dock is another criterion (see for instance [BG01]). The master’sthesis of Onur Ozturk co-supervised with M.-L. Espinouse [O08] enters in this type ofproblems in which the criteria to optimize is the weighted sum of all overlappings ofproduct flows.

33

34 CHAPTER 3. CROSS DOCKING

Scheduling of transshipment operations: The objective is to find the best sched-ule of trucks so that either the time or the cost related performance measures of thecross dock are optimized. Unlike the dock assignment problems, it is assumed that weknow exactly which precise dock will be used if a truck is scheduled, hence assignmentoperation is not considered. Most of the cross dock scheduling problems are NP hard.Nevertheless, polynomial algorithms can be developed for small-sized problems. There-fore, a group of studies have been conducted for single receiving and single shippingdock platforms (see for example [YE08, BPL07, BM07, CL09, BFS08, Sad09]. In ([5])and ([4]), we have also explored this simple cross dock.

Exploration of single receiving and shipping dock environments give interesting insightson the solution structure for scheduling problems at the cross docking facilities, howeverno practical application is possible. Therefore, for implementation purposes, multi-dock platforms need to be studied. Some recent work can be found for multi-dockcross docks (see [MSG05], [Boy09], [SC07], [CS09]). Our work on the multi-dockplatforms has started by the master’s thesis of Sylvain Bauchau [Bau07]. Since then,we have contributed to multi-dock cross docking literature by the following scientificproduction: ([3, 2, 6, 1]).

In the upcoming sections, we will first give detailed description of the problem typesstudied, present the proposed solutions, compare with the existing literature and highlightour contributions.

3.2 Problem description

Unlike the research work presented in chapter 2, our studies in the domain of cross dockingis not based on a field study. Only, the single dock problems which will be presented shortlyare inspired from the cross docking operations which are performed in an electrical appliancesproducer in Canada ([4]).

Therefore, in this chapter we present a cross dock environment with different assumptionswhich cover some typical cross docking operations. Some of these assumptions are inspiredfrom the industrial case mentioned above, some others are commonly seen in the alreadyexisting scientific literature. We tried to stay as generic as possible to describe a wide rangeof problem settings.

In this section, we will introduce these basic cross dock operations. We consider a crossdock with I ≥ 1 receiving and O ≥ 1 shipping doors. The products which transit thefacility are sent to one of the D different destinations. Each outbound truck serves a singledestination, d, d = {1, · · · , D}, while each inbound truck arriving to the platform may containproducts for several destinations.

Possible settings for the inbound docks, storage area and outbound docks are as follows:

• Inbound Area:

– Number of docks, I, is 1 or more.

– Arrival sequence of products can be fixed, i.e. the cross dock supervisor has nocontrol on the sequence, or flexible, i.e. the cross dock supervisor can change thearrival sequence. At first sight, a fixed arrival sequence may seem artificial. Nev-ertheless, they are typical to production cross docks, where the products coming

3.2. PROBLEM DESCRIPTION 35

out of production lines are cross docked immediately at the end of the lines. Inthis case, the arrival sequence of products are fixed and depends on the produc-tion plan which takes into account the availability of production resources and rawmaterials or components. The arrival sequence can also be known or unknown.For instance, in the case of a production cross dock, scheduling may be known inadvance, while for distribution cross docks, we may have limited knowledge on thearrival sequence of trucks.

– Unloading of arriving trailers may be in LIFO (Last In First Out) or in any order,which we will call flexible. LIFO unloading is observed when the trailer is unloadedfrom the rear, while the unloading can be in any order for trailers having lateralopenings.

• Storage Area:

– Storage may be allowed or not allowed as is the case for the perishable productscontext (see for instance [Boy09]). If storage is allowed, a holding cost, h, is paidper unit product. We will assume that this cost is independent of the storage time.

• Outbound Area:

– Truck replacement at the outbound side may bring some flexibility to cross dock-ing operations. Trucks which are not completely full and for which the remainingproducts are not yet at the cross dock can be temporarily removed from the out-bound docks to leave the place to another truck. As is the case for storage, undercertain circumstances, this preemption may be prohibited. Hence, two settings arepossible: either truck replacement is allowed or not allowed. If truck replacementis allowed, a cost of r units per replacement will be incurred.

– The order of loading can be FIFO (First In First Out) or in any order, here namedflexible. FIFO loading means that the first arriving products to a destination dare loaded onto the first truck departing for this destination. This guarantees thatproducts do not wait for long durations in the cross dock.

– Number of shipping docks, denoted O, is 1 or several.

The objective in our studies is to find the best schedule of transshipment operations (e.g.which pallets have to be loaded directly on outbound trucks, which pallets shall be stored,which outbound truck is scheduled at a give time, which is removed, ...) so that the total costof operations are minimized. The calculation of the total cost as well as the operations toconsider depends on the problem settings. For example, the total cost is only based on truckreplacement costs when storage is not allowed, or only on inventory holding costs when truckreplacement is forbidden, eventually on both when both truck replacement and storage areallowed. A summary of all possible problem settings are illustrated in figure 3.1. As can beobserved, based on different possible settings, 28 = 256 problems can be generated. We haveproposed solutions for some of these problem settings. Next, we will give an overview of theproposed solutions. The following assumptions are common to all of these studies.

A1: The products are identically conditioned in unit size pallets. Hence, all transship-ment operations on every pallet are done in an identical unit time, τ . That is, a palletcan be unloaded, then either be directly loaded into an outbound truck or transfered


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Fix

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ed

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wed

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flex

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Figure 3.1: 28 problem settings recorded in [Lar08]

3.2. PROBLEM DESCRIPTION 37

to the storage area in τ time units. Similarly, a pallet which is temporarily stored willtake τ time units to load into an outbound truck. Without loss of generality, we assumeτ = 1.

A2: We assume that all departing inbound trucks are immediately replaced by a newone. We note that this assumption can be relaxed technically, by inserting emptyinbound trucks in the arrival sequence to fill the time gap between inbound trucks.

A3: There is sufficient workforce to load/unload all docked trailers at the same time.Hence, a trailer assigned to a dock does not wait for the availability of a materialhandler.

A4: The pallets in the inbound trucks have priority on the pallets already stored inthe cross dock. This assumption is a logical consequence of the cost structure andassumption A1. Since the holding cost h is per unit pallet stored and the time of thestorage is ignored, a pallet staying in the cross dock for the whole day will cost the sameas the pallet stored for a few minutes. Furthermore, any pallet transferred directly froman inbound to an outbound truck will take only τ time units compared to 2τ time unitsfor the stored items.

A5: The outbound truck fleet is well dimensioned with interchangeable standard trucksso that no time is lost waiting for the arrival of an outbound truck.

A6: All outbound trucks are loaded to their maximum capacity (with the exception ofthe final trucks leaving at the end of the day).

A7: All products arriving to the cross dock during the day should leave the cross dockthe same day.

Main notation used in this chapter is summarized in table 3.1. Additional notations willbe introduced later on for different problem types.

Notation Description

I number of receiving doors

O number of shipping doors

D number of destinations

h holding cost

r truck replacement cost

S an input sequence (granularity is based on problem type)

τ unit time of each transshipment operation

P kd kth outbound truck to destination d, d = 1, · · · , D,

md number of outbound trucks serving destination d

Πi ith inbound truck in the arrival schedule of the trucks, with i = 1, · · · , n,

xid quantity of pallets in Πi for destination d

κkd capacity of P kd

xkid number of pallets coming from Πi loaded on P kd .

Table 3.1: Input Parameters


For multi-dock problems the input sequence S is defined as a matrix and is discretized byτ . By assumption A1, each discrete unit in S corresponds to the movement of a unit productas well. Hence, the tth column of S gives the set of all pallets to be unloaded at time intervalt, t = 1, 2, · · · , T at all receiving docks I. If the arrival sequence is known and the unloadingis LIFO, this sequence will be completely fixed.

In the case of single-receiving single-shipping dock problems this level of granularity is notnecessary. S, in this case, is a vector of xkid values.


In this section, we present our solutions to some of the problems depicted in figure 3.1.In section 3.3.1, solution methods related to a simple cross docking environment, namelysingle receiving and shipping door cross docks, are presented. Section 3.3.2 regroups methodsfor multi-dock problems. The methods presented in sections 3.3.1 and 3.3.2 consider onlyminimization of costs generated by cross docking operations. We present some exploratorystudies in section 3.3.3 which take into account time related performance measures in theobjective function and the dock assignment problems.

3.3.1 Single receiving and single shipping door cross docks

Most of the studies existing on the scheduling problems in cross docks treat this simple caseof single receiving and single shipping dock context.

In this simple context, we have studied several cases in the thesis of Rim Larbi co-supervised with Bernard Penz [Lar08]. These cases have not been studied before:

1. For the cases where storage is not allowed: we have studied the fixed and known arrivalsequence, with LIFO unloading where truck replacement is allowed. This is a specialcase of the problem which will be discussed in section 3.3.2 and the same methods canbe used for its resolution.

2. For the cases where storage is allowed,

(a) and truck replacement is not allowed: Two optimal methods based on constructionof graphs are proposed for the fixed and known truck arrival sequences with eitherLIFO or flexible unloading policies, respectively. Complexity of these methods arepolynomial.

(b) and truck replacement is allowed: we studied the case where the arrival sequenceof trucks are fixed and the unloading is flexible.

In this section, we will not detail all of these polynomial time algorithms. We will onlydetail the case (2.b). Namely, the storage and truck replacements are allowed, thearrival sequence of trucks are fixed and the unloading is flexible. The originality ofthis case with respect to the other ones is the consideration of the availability of differentlevels of information on the arrival sequence:

• The off-line case: the inbound truck sequence is totally known i.e. the order of inboundtrucks is known and the quantities loaded in each inbound truck for each destinationare also known in advance. The preliminary results on this case is presented in ([4]).


• The on-line case: the inbound truck sequence is totally unknown i.e. the order andthe contents of incoming trucks are unknown. This information is revealed when thetruck arrives to the cross dock. Only the daily quantities to ship to each destinationare known in advance.

• The semi on-line case : the inbound truck sequence is partially known i.e. whenan inbound truck arrives at the cross dock, the contents and the order of the next Zinbound trucks are revealed.

For the outbound side, we consider that the loading follows a FIFO rule.Given the above problem setting the objective is to find the optimum sequence of outbound

trucks (and thereby the transshipment operations) such that the sum of the total inventoryholding and truck replacement costs are minimized. The solutions for the on-line and thesemi on-line cases are presented in ([5]).

The off-line case

For the off-line case, we propose an optimal graph based solution which is proven to bepolynomial time ([4]). We briefly present this method below.

Since, the arrival sequence of inbound trucks as well as their load is entirely known andthe loading policy is FIFO, the input sequence S can be constructed as a fixed sequence ofpallets unloaded from Πi which shall be loaded on P k

d . This is a sequence of xkid.The knowledge of the outbound trucks occupying the shipping door at the beginning and

at the end of the unloading of each inbound truck Πi permits us to determine which quantitieshave to be stored and which ones have to be directly loaded. We formally define such a truckas follows:

Definition 4. A triplet is defined as (Πi, Pkd ,Πj) which denotes that the truck P k

d occupiesthe shipping door from the end of unloading of Πi to the beginning of unloading of Πj (withi < j).

Of course, we are not interested in any kind of triplets. A triplet (Πi, Pkd ,Πj) is of interest

if P kd can receive merchandise from the inbound trucks Πi to Πj . Property 1 below helps us

to identify the set of appropriate outbound trucks and to discard some of the triplets fromthe solution space. The proof is provided in ([5]).

Property 1. If P kd is at the shipping dock at the end of the unloading of Πi and if there exist

products in Πi+1 to be loaded on P kd , the dominant solution is to keep P k

d at the shipping dockbetween the ending of unloading of Πi and the beginning of unloading of Πi+1.

Based on property 1 we define the notion of a valid triplet as below:

Definition 5. Let Πmin (resp. Πmax) be the first (resp. final) inbound truck from which thetruck P k

d can receive its first (resp. final) load. Πmin and Πmax are deduced from the inputsequence S. For each couple (Πi,Πj |i ≥ min and j ≤ max), and for at least one l suchthat i ≤ l ≤ j, if ∃xkld 6= 0 then assignment of the outbound truck P k

d gives a valid triplet(Πi, P

kd ,Πj) since P k

d can receive loads from trucks Πi to Πj. For a given valid triplet (Πi,P kd , Πj), the following transshipment operation occur at the cross dock:

• the outbound truck P kd is at the shipping door during the ending of the unloading of Πi

and before the unloading of xkid;


• the outbound truck P kd is at the shipping door during the beginning of the unloading of

Πj and after the unloading of xkjd;

• the remaining merchandise (xbla) with i < l < j and (a, b) 6= (d, r) is temporarily storedin the cross docking platform. A holding cost for this quantity is paid.

Using definition 5 we can enumerate all valid triplets. The total cost of a valid triplet(Πi, P

kd , Πj) is calculated either by equation (3.1) if P k

d is filled up to its capacity duringthe interval (Πi,Πj), or by equation (3.2) if P k

d is not full during this interval. Equations 3.1and 3.2 basically calculate the holding cost of all pallets which have to be stored since theycan not be loaded on P k

d . We note that no replacement cost is charged for outbound trucksleaving the dock when they are full. Therefore, an additional replacement cost of r is paidfor a partially full P k

d (equation 3.2) since we know that we will have to bring it back to thedocks at a future time.

C1 = h

j−1∑l=i+1

∑(a,b)6=(d,r)

xbla (3.1)

C1 = h

j−1∑l=i+1

∑(a,b)6=(d,r)

xbla + r (3.2)

The best schedule of transshipment operations is a sequence of valid triplets {(Π1, Pk1d1,Πi1),

(Πi1 , Pk2d2,Πi2), (Πi2 , P

k3d3,Πi3), · · · , (Πil , P

k1d1,Πn)}, with 1 < i1 < i2 < i3 < · · · < il < n for

which the total inventory holding and truck replacement costs are minimized.Up to now we have just presented how valid triplets are constructed and valuated. Between

two valid triplets (Πi−q, Pk1d1

, Πi) and (Πi, Pk2d2

, Πi+r), we have to establish an optimal sequence

of operations as well. According to the definition of a triplet, xk1id1 is directly loaded into P k1d1

and xk2id2 is directly loaded into P k2d2

. For all other quantities xkid such that (d, k) 6= (d1, k1)

and (d, k) 6= (d2, k2), we have to choose if xkid is loaded directly in P kd or if it is stored

temporarily. Two types of xkid are to be considered. If xkid completes P kd , then P k

d is placedat the shipping door, xkid is loaded directly and the cost is zero because the truck leaves thecross dock definitively. If xkid does not complete the truck, P k

d can be placed at the shippingdoor incurring a cost r, or xkid is stored incurring a cost h×xkid. The best solution is obviouslythe one having the min(r, h× xkid). As the decisions for two different xkid are independent, itis straightforward to compute a sequence of minimum costs. This minimum cost is given byequation (3.3), where ∆ is the set of trucks P k

d such that (d, k) 6= (d1, k1), (d, k) 6= (d2, k2)and xkid does not complete the truck, P k

d .

C2 =∑∆

min(r, h× xkid) (3.3)

Algorithm 2 finds an optimal sequence of outbound trucks and traces the transshipmentoperations. The complexity of the algorithm is in O(D2n2).

The on-line case

For the off-line case, we have complete information on the order of all incoming trucks andtheir loads. For the on-line case, we will assume that only the daily quantities xd for each


Algorithm 2 An optimal polynomial time algorithm for the off-line problem

1: Compute all valid triplets (Πi, Pkd , Πj)

2: Generate nodes (Πi, Pkd , Πj , in) and (Πi, P

kd , Πj , out)

3: Link nodes (Πi, Pkd , Πj , in) and (Πi, P

kd , Πj , out) by an edge valuated by C1

4: Link nodes (Πi−q, Pk1d1

, Πi, out) and (Πi, Pk2d2

, Πi+k, in) by an edge valuated by C2

5: Create a node begin and edges from begin to all the nodes (Π1, P kd , Πi, in) valuated by

C2

6: Create a node end and edges from all the nodes (Πi−q, Pk1d1

, Πi, out) to end valuated by0

7: Find a shortest path in the graph constituting a sequence σ of valid triplets (Πi, Pkd , Πj)

8: rebuild from σ a sequence of outbound trucks

destination d are known. Similar to the off-line case, we assume that the products are loadedaccording to first-in first-out (FIFO) policy. Each truck for a destination d are assumed tobe fully loaded up to their capacity.

In the on-line case, for each quantity of products xid arriving to the cross dock by theinbound truck Πi and to be shipped to the destination d, there are two possibilities :

• xid × h ≥ r : In this case, the best solution is loading xid directly on to an outboundtruck;

• xid× h < r : In this case, the decision depends on the future activities at the platform.If the next q inbound trucks Πi+1, Πi+2,... Πi+q , transport a quantity x(i+j)d, 1 ≤j ≤ q, for destination d, a good solution might be to place the outbound truck servingdestination d on the shipping door in order to load x(i+j)d, 1 ≤ j ≤ q. However, the loadof the next q inbound trucks are unknown. Therefore, the efficiency of such a solutiondepends on the probability of the arrival of products in destination to d as well as theexpected costs associated with this solution.

As seen above, the decision is simple in the case when xid× h ≥ r but rather complicatedfor the case when xid × h < r. To solve the on-line scheduling of cross dock operations, wepresent a heuristic based on a probabilistic decision rule to be applied when xidh < r. Thisdecision rule is stated as follows: At the end of unloading of an inbound truck Πi, the outboundtruck which has the highest probability to be fully loaded with the minimum expected cost ischosen to be placed at the shipping door. The calculation of the related probabilities and theexpected costs are explained in detail in ([5]). Algorithm 3 details this heuristic.

The semi on-line case

In the semi on-line case, we have only partial information on the arrival sequence. We considerthat when an inbound truck Πi arrives at the cross dock, only the sequence of the next Zinbound trucks (Πi+1, · · · , Πi+Z) and their contents are known. For Z = n− i, the problemconverges to the off-line problem while for Z = 0 the semi on-line problem is equivalent tothe on-line problem.

For the resolution, we propose two different heuristics. The first one is uniquely based onthe off-line solution approach on a rolling horizon. The second one is a hybrid heuristic basedon both the off-line and the on-line solution approaches:


Algorithm 3 A stochastic algorithm for the on-line problem

Let Rd be the remaining quantity of products to be sent to the destination d during theplanning horizonRd be remaining quantity of products to be sent to all other destinations d′ (d′ 6= d).

κkd be the remaining capacity of P kd .

EC(d, i) be the expected cost placing P kd at the shipping dock when Πi is at the receiving dock.

For the inbound truck Πi, i = 1 · · ·n do;

1: For all destinations d present in Πi and where xid × h ≥ r, load xid directly on thecorresponding truck P k

d . In this case a cost of r will be paid for each truck replacement ifthe current truck at the stack door is not full. If no such destination exists go to step 3.

2: Update κkd, Rd and Rd.3: For all destinations d present in Πi, calculate the expected costs EC(d, i).4: Choose P k

d having the smallest EC(d, i). This P kd is assigned to the shipping dock as the

final truck when Πi is at the receiving door.5: All xid in Πi which cannot be directly loaded on a truck are stored in the cross dock and

a cost of xid × h is paid.

6: κkd, Rd and Rd.

• Heuristic based on real partial information on a rolling horizon: This firstheuristic is totally based on the graph based solution developed for the off-line case.When an inbound truck Πi arrives at the cross dock, the sequence and the load ofthe following Z inbound trucks are assumed to be known. Heuristic 1 consists in con-structing the graph for the partially known sequence of upcoming arrivals, i.e. for thesequence Πi, · · · , Πi+Z . A single decision is taken for the subsequence (Πi,Πi+1) usingthe available information. The procedure is repeated on a rolling horizon of Z trucks,i.e. for the next subsequence Πi+1 · · ·Πi+Z+1.

• Heuristic based on real and estimated partial information on a fixed horizon:The idea for this second heuristic is to offer a complete outbound truck schedule for thepartially known sequence of Z incoming trucks. Hence, the outbound truck sequencewill be calculated for the inbound trucks Π1, · · · ,ΠZ+1, then for ΠZ+2, · · · ,Π2Z+2 andin general terms for Π(a−1)Z+a, · · · ,ΠaZ+a), a ∈ N and a ≤ n

Z+1 . For the calculation ofthese partial schedules we apply the optimal off-line algorithm (see algorithm 2). Then,in order to find the most likely outbound truck to assign at the shipping dock at theend of a sequence of Z arrivals, we use the stochastic on-line algorithm (see algorithm3). The procedure is repeated for the next Z inbound trucks until all n incoming trucksarrive to the cross dock.

A battery of tests has been run to evaluate the performance of the algorithms proposedfor the on-line, off-line and semi-online cases. Being polynomial, the algorithms are quitefast for large instances. In this case, a point of interest is to test how the quality of solutionchanges by the amount of information that we have on the arriving process. Therefore, thetests are conducted for variable Z. Aside from Z, parameters influencing the performance ofthe methods are the number of destinations d and the length of the inbound truck sequencen. In the experiments, we assume n = 100 and we vary d between 2 and 25. The major


findings are as follows. The interested reader can refer to ([5]) for details.

• We remark that in the total absence of information (i.e. Z = 0), the costs incurred bythe solutions obtained via the heuristics are almost twice the cost of the solution foundunder complete information. However the solutions converge to the optimal rather fast.The difference drops to 20 to 30 percent with the information of the next arriving truck(i.e. Z = 1).

• For the semi on-line problem, heuristic on a fixed horizon gives better results thanheuristic with rolling horizon. This is interesting because, one might think that gener-ating new schedules at each inbound truck arrival with the updated information will givemore precise solutions. We observe though that in the case of rolling horizon, since asingle decision is made for the next assigned truck, truck replacements are more frequentand the overall solution is less cost efficient. In practice, heuristic with fixed horizonis also much easier to implement in a real cross dock environment since it generates aschedule for a batch of truck arrivals meaning that a series of transshipment decisionsare given for a longer period of time.

• Another conclusion is that the distant future information does not improve the solution.For large d if we have partial information on the next 14 inbound trucks, the schedulingof the transshipment operations is almost as good as if we have complete informationon the incoming truck sequence. The difference with the optimal solution is less than5%. This limit is even lower (i.e. Z ≥ 6) if the number of destinations is low.

3.3.2 Multi-door cross docks

Scheduling operations in multi-dock environment is much less studied than the single doorcross docks. To the best of our knowledge this problem is treated only by [CS09], [MSG05]and [Boy09].

In [CS09], Chen and Song present the cross docking scheduling problem as a two-stageflow shop problem with parallel machines. Indeed, this analogy was first established by Chenand Lee in [CL09] where each stage of the problem contained only a single machine (i.e. singleinbound and outbound dock). They showed that the problem is NP-hard in the strong sensefor this simple case. In [CS09], each stage corresponds to either inbound or outbound side ofa cross dock, the machines and the set of jobs are analogous to inbound or outbound docksand the trucks to unload or load, respectively. At least one of the stages is allowed to havemore than one machine. That is either inbound or outbound side is multi-dock. The objectivehere is to find the best schedule of inbound and outbound trucks so that the makespan ofoperations is minimized. The authors assume that the inbound trucks are available at time0 (i.e. arrival sequence is flexible and known, with flexible unloading of pallets) and each ofthe inbound trucks carry pallets only for a single destination. We recall that, each inboundtruck in our case may carry several pallets to different destinations and the objective is a costrelated one.

The other studies on multi dock environment are specific to some industrial settings(see [MSG05] and [Boy09]). In [MSG05], McWilliams et. al. present the parcel hubscheduling problem common in parcel delivery industries such as the postal services. Theobjective of this study is very similar to the scheduling problems in multi door cross docks,i.e. finding the best schedule of inbound trucks so that the makespan of the parcel transfer


operations is minimized. However, the environmental setting has some specificities. Oneof the characteristics of a parcel hub compared to a classical cross docking platform is thetype of materials handling system utilized. In a parcel hub, the flow of the materials issupported by a network of fixed conveyor belts. Temporary storage of pallets are not explicitlyconsidered. The main focus is on the congestion of the fixed conveyor belts by the untimelyunloading of the incoming parcels. Therefore, we consider the parcel hub scheduling as aspecial case of cross docking. Another special case is studied in [Boy09] for a food industrycross docking facility. In this case, the inventory holding is strictly forbidden. The authorpresents a dynamic programming approach as well as heuristics based on simulated annealingto schedule the inbound and outbound trucks. Three time related objective functions areconsidered: minimization of total processing time, total flow time and the total tardiness.

For the multi-dock environments, we will consider that D > O (since the case with D ≤ Ois trivial) and I ≤ O to ensure that traffic intensity inside the cross dock does not generateinfinite stock when storage is allowed and to avoid infeasible solutions when storage is notallowed.

Cases where storage is not allowed

In the category of multi-door cross docks, we first consider a particular case where the storageis not allowed. This means that, a scheduled inbound truck is unloaded if and only if thecorresponding outbound truck is at the shipping docks. Truck replacement is allowed. Other-wise, obtaining a feasible solution may be difficult depending on the arrival sequence. We willfirst consider that the arrival sequence is fixed and the unloading is LIFO. Hence, wehave no possibility to control the inbound schedule and S is completely fixed. The objectiveis to find the sequence of outbound trucks such that the total cost of truck replacements isminimized.

In the PhD thesis of Rim Larbi [Lar08], we have shown that the above described problem isequivalent to the problem of minimizing the number of tool switches in flexible manufacturingsystems [TD88], under the additional assumption that each destination d is served by a singletruck. That is, the capacity of a given outbound truck, κkd is infinite for r = 1. The problemof tool switching is described as follows:

Let L be the number of parts to be processed on a flexible machine. Each part requiresa set of jobs needing Q tools to be used. Total number of tools available in the workshop isW . The machine can receive at most R tools at a time. The manufacturing process of a partcan start only if all tools required for a given part is available on the flexible machine (i.e.Q ≤ R by definition). If the machine is at its full tool capacity and all tools required for themanufacturing of a part are not on the machine, unused tools are to be switched. The toolswitches being automatically done, the duration of switching is considered constant. Hence,only the number of switches is important. It is assumed that each tool is unique. For a givenfixed sequence of parts to be processed, the goal is to determine which tools to install on andwhich ones to remove from the machine at each part arrival while minimizing the number oftool switches for the whole sequence of arrivals.

In the problem of cross docking, at each time unit t, we have I incoming pallets whichare to be directly loaded on a set of outbound trucks Pd . Hence at each time unite t a setof outbound trucks has to be selected to assign on the O shipping docks. If we consider eachtime unit t as the lth part to manufacture in the sequence of L parts, each pallet arriving tothe cross dock at time t as the set of tools Q required to manufacture each part, number of


destinations D to be served as the total number of tools W available at the workshop, andthe number of shipping docks O as the tool capacity R of the flexible machine, we have anequivalence between the two problems.

Tang and Denardo [TD88] has resolved this problem using and algorithm which is namedKeep Tool Needed Soonest (KTNS), The idea is to keep the tool required soonest in thesequence of parts and the main steps are as follows:

1. Insert a tool only if it is needed by the job to be executed.

2. If the tool capacity of the machine is full and a tool has to be replaced to release theplace for another tool, then remove the tool which is needed latest by the sequence ofparts.

This algorithm is polynomial where at most O(LQR) operations are needed. If the toolcapacity R of the machine is a known input parameter, then the complexity reduces to O(LQ).Optimality of this algorithm is proven by Crama et. al. [CKOS94]. Thanks to the equiva-lence described above, we can apply the KNTS rule to find an optimal solution in polynomialtime for the cross docking problem.

Later on, Privault and Finke [PF00] have shown that the tool swithing problem is equiv-alent to the bulk requests in k-server computing [Bel66]. Consequently, our problem isanalogous to this problem as well.

If we relax the assumptions on the sequence of arrivals and the unloading process, (i.e.accepting flexible sequence of arrivals or unloading) the equivalence no longer holds for I ≥ 2.The reason is that if we permute some trucks or order of pallets inside an inbound truck thenthis means that we are changing the set of tools needed by a given part, i.e. Q, and hencetwo problems are no longer equivalent. For I = 1, these permutations will not affect Q, theywill only change the order of parts and hence equivalence still holds.

The tool switching with flexible arrival of parts is shown to be NP-hard by Garey andJohnson [GJ79] and many heuristics have been proposed for its resolution either for toolswitching or bulk requests in multiple-server context: Heuristics based on travelling salesmanproblem ( [TD88, CKOS94]), greedy methods like 2-Opt [CKOS94], meta-heuristics liketabou search [Fol92], rules such as regrouping similar parts [Pri94] are only a few to name.

Since the equivalence is still valid for I = 1 and many heuristics already exist, one of theseheuristics can be used to solve the cross docking problem with no storage and flexible palletarrivals described above. Therefore, we haven’t proposed additional heuristics for this case.Instead, we have worked on problems for cross docks where the storage is allowed.

Cases where storage is allowed

In this section, we will discuss the case where storage is allowed. In this case, our operationsscheduling problems in cross docking differs from the known machine scheduling problems.

For the inbound side, we will assume that the arrival sequence of trucks are knownand fixed and the unloading is LIFO. These settings guarantee that S is entirely known.For the outbound side, we will consider that truck replacement is allowed and the loadingfollows a FIFO rule.


Given the above problem setting with a known arrival sequence of pallets S at the receivingdocks at any time interval t, t = {1, · · · , T}, the objective of here is to find the optimumsequence of the set of outbound trucks that should be present at the shipping doors at anytime interval t, t = {1, · · · , T} such that the sum of the total inventory holding and the truckreplacement costs are minimized.

Optimal solutions for cases where storage is allowed

Our first attempts to solve cross dock problems with storage for multi-dock environments is bythe master’s thesis of Sylvain Bauchau, co-supervised with Bernard Penz [Bau07]. During thisstudy, a dynamic programming (DP) based model has been proposed for the above problem.The initial research findings are published in ([1]) and an extended version appeared recently([3]).

The notations specific to the model are as follows:

Xt : set of states at each time interval t, t = {1, · · · , T}.

xt = (t, Zt, Yt) ∈ Xt: a tri-dimensional state vector where;

t is the time interval of the input sequence S,

Zt is the set of outbound trucks (or destinations) present at the shipping doorduring the time interval t.

Yt is a vector of dimension D which keeps track of the quantity of pallets tem-porarily stored for each destination, d = {1, · · · , D}.

CZt+1(xt): the cost incurred at time interval t+ 1 given that the system was in state xtat time interval t and we have decided to assign the set of outbound trucks Zt+1 at theshipping docks at time t+ 1. CZt+1(xt) is calculated by equation 3.4.

CZt+1(xt) =

D∑i=1

max{0, (yt+1i − yti)} × h+

O∑j=1

1zt+1j /∈Zt

× r (3.4)

where 1 is an indicator function which takes the value of 1 if zt+1j /∈ Zt and 0 otherwise.

For each pallet in destination to d in sequence S at time interval t+ 1, we may decide toassign a truck for destination d at the shipping docks. If such a truck is already available atthe docks at time t, we may keep it at time t+1 as well and transfer the pallets from receivingto shipping docks with 0 cost. If on the other hand, no such truck is already available at theshipping docks, we have two possibilities; either we store the pallet (i.e. yt+1

d = ytd + 1 andincur h), or remove one of the current outbound trucks to affect the corresponding truck, toa shipping door (i.e. 1zt+1

d /∈Zt= 1 and incur r).

As seen in equation 3.4, CZt+1(xt) sums up the total cost of storage for the pallets whichare temporarily stored and the total cost of outbound trucks which are replaced during thetime interval t + 1, given that the system was in state xt at time t and the set of outboundtrucks Zt+1 are assigned at the shipping docks at time t+ 1.

Let P (xt+1) be the set of all states xt, xt ⊂ Xt, which are the predecessors of state xt+1.Using equation 3.4, the DP model to find the global cost up to time interval t+ 1 is given inequation 3.5.


ft+1(xt+1) = min∀xt∈P (xt+1)

{CZt+1(xt) + ft(xt)} (3.5)

with f0(x0) = 0.The recursive function in equation 3.5 generates a graph with exponential complexity. The

following properties are used to reduce the size of the graph generated by equation 3.5 withoutaffecting the optimality of the solution. The conservation of optimality for each property isproven in ([3]) and will be omitted here. We note that the optimality of property 4 appliesfor infinite T and κkd. For finite truck capacity κkd, additional decisions are to be consideredrelated to the remaining capacity of the trucks. But these are mainly borderline effects. In([1]) a series of experiments have proven that the relative deviation from optimal in case ofthe bounded truck capacity is negligible.

Property 2. A node n1 of the graph generated by equation 3.5 is dominated by another noden2 if and only if the following conditions are satisfied.

1. n1 and n2 have the same time stamp, t and the same set of outbound trucks present atthe shipping doors.

2. For each destination d, the quantity of the temporarily stored pallets for n1 is greaterthan or equal to the quantity of the temporarily stored pallets for n2.

3. Cost generated by n1 is strictly greater than that of n2.

Property 3. Let n1 and n2 be two nodes in the generated graph which have the same timestamp t. The nodes which succeed n1 are exactly the same as those succeeding n2 if and onlyif the vectors Yt in n1 and n2 are identical.

Property 4. Let t be a time instant in the input sequence S of length T and d be a destinationfor which an outbound truck is present at the shipping door at time t. If a pallet to destinationd is present at t + 1 as well, the truck to destination d is kept at the shipping doors duringt+ 1.

A battery of tests has been run to test the performance of the DP model by varying theparameters (I,O,D, T ). For detailed numerical results the reader can refer to ([1]). Here, wewill only summarize the major findings of these numerical results:

1. On the average 41% to 62% of the potentially generatable nodes in the graph areeliminated using the properties.

2. Nevertheless, the kept nodes may go up to an order of 106 nodes even for small sizedproblems (e.g. (I,O,D, T ) = (1, 2, 9, 50)).

3. The execution time of the model depends highly on the number of nodes of the graph.To give a global idea of the problem sizes that can be solved optimally in reasonabletime, we give some limiting parameters in table 3.2

4. Finding an optimal solution gets difficult as I approaches O. For instance, in table 3.2 weobserve that for (I,O,D, T ) = (7, 9, 10, 100) the execution time is only 40.945 seconds,while the program cannot find a solution for (I,O,D, T ) = (8, 9, 10, 100) even afterhours of execution. This is logical since the input traffic of pallets is barely absorbed


by the outgoing trucks which results in the saturation of the cross dock. We note that,this phenomenon is observable in real cross docks as well. Therefore, a rule of thumbin practice is to design cross docks with O ≥ 2I [Nap00].

5. Closer the number of shipping docks O to the number of destinations D, an optimalsolution can be found faster. A cross dock is hence efficient if |D − O| is low. Wecan explain this by an example. For instance for (I,O,D, T ) = (1, 2, 9, 50) in table3.2, the execution time is 5363.772 seconds. If O is increased to 7, the execution timereduces to around 8.7 seconds on the average. Mathematically, the number of possiblecombinations are equivalent (i.e. C9

2 = C97 ), however, in practice it is much easier

to affect 9 destinations to 7 docks than to 2 docks. Indeed some of the potentialcombinations in C9

7 will be eliminated much easily by the properties.

I O D T Time (s)

1 2 3 5000 1383.106

1 2 9 50 5363.772

2 4 6 50 4679.657

3 7 8 1000 1983.282

4 7 8 600 1034.437

7 9 10 100 40.945

Table 3.2: Some limiting input parameters for reasonable execution times (average of 10instances)

Heuristic solutions for the cases where storage is allowed

As we have seen in the previous section, optimal solutions can be found for multi-dock prob-lems only for small size problems. Therefore, we have looked for heuristic solutions in ([6])and ([2]). The general idea of the heuristics is to limit the level of some input parameter inorder to remove additional nodes from the DP model.

In the DP model a state xt is represented by 3 components, namely the time interval t,set of outbound destinations Zt and the amount of products already stored, Yt. Among these3 components, we will put emphasis on Zt and Yt because these 2 parameters are interestingto control in practice, as well. For instance, reducing the number of units which can be storedin the cross dock will not only have a positive effect on the performance of the DP model,but it may also improve the internal performance of the facility: Smaller stock level in thefacility diminishes the level of congestion inside the facility and hence augment the speed ofthe product flow, which in turn increases the productivity of the facility.

In algorithm 4 we are limiting the maximum level of storage by a quantity Smax calculatedsimilar to a classical EOQ (Economic Order Quantity) model with finite production rate (seeequation 3.6):

Smax =T (O − I)I

O(3.6)

Algorithms 5 and 6 seeks to reduce the possible combinations which arise due to Zt, theset of outbound trucks present at time t. If no products are expected to arrive for destination


Algorithm 4 : DP bounded on the level of stock

Let γ be the percentage of Smax which will be allowed at the cross docking facility. Repeatthe following steps for each node generated by the original DP.

1: At a given stage t, generate all nodes (or equivalently states xt ∈ Xt as in the originalDP.

2: If∑d=D

d=1 yd > γ × Smax where yd represents each cell of the vector Yt, then remove thisnode from the graph.

d during a time period, there is no need to assign an outbound truck for this destination.Similarly, if there is an important arrival of products for a destination d′, it is logical to havesome outbound trucks for d′ be present at the docks for direct loading. Algorithm 5 forcessome outbound trucks (resp. destinations) to be present at the docks, while algorithm 6 banssome outbound trucks from being present.

Algorithm 5 : Forcing some destinations at the outbound docks

Let Ht be the subset of destinations to be present at the outbound docks at time t.F td be the fill rate of the destination d at time t. This is a measure of the concentration of

pallet arrivals for destination d during a time window [t − b, t + b]. b is a parameter whichcan be fixed by the decision maker.nt be the number of destinations forced to be present at the outbound docks at time t.N be the maximum number of destinations which are allowed to be forced at any time.Repeat the following steps at each instant t of the original DPmodel.

1: Calculate F td as follows: For each interval [t − b, t + b], the instances are weighted such

that the instance t has the highest weight, wt. The weights will be reduced as we moveaway from t. That is, wt > wt−1 = wt+1 > wt−2 = wt+2 > · · · . F t

d = Σi=t+bi=t−b(wi × |di|)

where |di| is the number of pallets in destination to d at time interval i.2: If F t

d ≥ α then d is a candidate destination to be included in the set Ht. Otherwise, discardd from further consideration. Here, α is assumed to be a good score and is decided bythe decision maker.

3: If the number of candidate destinations are greater than N then only N destinations whichhave the highest F t

d values are included in Ht. Otherwise, all candidate destinations arethe members of Ht.

4: Remove all nodes xt generated at time t for which d ∈ Ht and d /∈ Zt.

Limiting the size of Ht has two advantages: First of all, if we fix too many destinationsto be present at a given time, we will unnecessarily eliminate too many nodes from the DPmodel, some of which may be on the optimal shortest path. Secondly, this limit helps us toregulate nt in a dynamic way at each t. For instance, let N = 2, then 0 ≤ nt ≤ 2 ∀t.

Algorithm 6 is very similar to algorithm 5 with the exception that we avoid some desti-nations to be present now.

Similar to algorithm 5, we limit the number of banned destinations by M , i.e, mt = |Ht| ≤M < D −O. During the execution of the algorithm, mt will vary dynamically as is the casefor nt in algorithm 5.

Finally, we have tested simply constraining the number of nodes at each step of the DPmodel using algorithm 7.


Algorithm 6 : Avoiding some destinations at the outbound docks

Let Ht be the subset of destinations. mt be the number of destinations banned at theoutbound docks at time t.M be the maximum number of destinations which are banned at any time.Repeat the following steps at each instant t of the original DPmodel.

1: Calculate the fill rate F td as in algorithm 5.

2: If F td ≤ β then d is a candidate destination to be included in the set Ht. Here, β is

assumed to be a bad score and is chosen by the decision maker.3: Remove all nodes xt generated at time t for which d ∈ Ht and d ∈ Zt.

Algorithm 7 : DP bounded on the number of nodes

Let ζ be the percentage of nodes which will be kept at each stage. Re-peat the following steps for each stage t of the original DP, t ={0, · · · , T}.

1: For a new stage t + 1, generate all the nodes which stem from stage t as in the originalDP.

2: For each new node in stage t+ 1 calculate the global cost as in equation 3.5.3: At stage t+ 1, keep only ζ percent of the nodes having the least global cost.

A battery of tests has been run on 7 different input sets with 10 different pallet ar-rival sequences for each input set. Our random input sequence generator is available onhttp://www.g-scop.fr/∼gaujalg/Xdock.html for the researchers in the domain. For each oneof these 7 × 10 tests, the algorithms are run for different values of α, β, γ, ζ, b, N and M .The size of the parameters in the instance sets are kept small so that the performance of eachheuristic can be compared with the optimal solution found by the DP model. Here, we willonly summarize the major findings of these numerical tests. For detailed numerical results,the reader can refer to ([6]) and ([2]).

• Heuristic which bounds the number of nodes generated at each stage of the DP modelis overruled by the other heuristics. Even for small ζ, the solutions may be degradedfor some instances since the nodes are removed in a arbitrary way. Hence, below wewill give the performance results only for the other 3 heuristics.

• In most of the cases there is at least one heuristic which can obtain near optimal solutions(less than 5% to optimal) twice as fast as the DP model.

• However, there is no single heuristic which performs better than the others for all in-stances. This is most probably related to the parameterizing process. The performanceof the heuristics depends heavily on the choice of the parametes α, β, γ, ζ, b, N andM .

• For the heuristic which forces the outbound trucks to be present, N can be parameter-ized as a function of O. α is quite related to how weights are assigned for each time slotin the moving window [t− b, t+ b] for the calculation of F t

d. Therefore, we believe thatit can be calibrated, as well.


• For the heuristic which bans some outbound trucks, β = 0 seems to be a logical as-signment which works well. That is, if a certain destination d is not present during agiven time interval do not consider trucks to this destination. However, the length ofthe moving time window shall be calculated. For most of the experiments b = 4 or b = 5work well and can be taken as an initial point.

• Heuristic which constrains the storage level is very sensitive to γ and depending on theinstance set, appropriate γ values may vary a lot. It seems rather trickier to find theappropriate values for γ. For this heuristic we observe that the gap-to-optimal curve ispiece-wise linear, meaning that the distance to optimal solution is stable for a range of γvalues, then there is a δ amount of degradation in the performance and the performancestays stable for another range of γ values. On the practical side, this observation is quiteinteresting. For instance, for a given range of γ1 < γ2, < ... < γn values we obtain equaloperational costs. Hence, the operations manager has some flexibility on how to allocatethe extra space, should he decide to stock only γ1%. Similarly, he observes that storinga little more does not increase his operational costs. He may then use this flexibility onthe truck assignment process. Therefore, we believe that testing different γ values shallnot necessarily be considered as a waste of time.

Since none of the heuristics is dominating the others for all instances, we have also testedsome hybrid heuristics in ([2]) where more than one of the above heuristics are applied at thesame time, at each decision node of the DP model. The performance of hybrid heuristics arecompared to the performance of each individual heuristic for the same instances.

• Hybrid heuristics are especially interesting when the size of the instance is increas-ing. Optimal solutions are obtained using hybrid solutions faster than each individualheuristic.

• We also observe that if the gap to optimal with heuristics i and j are u and v percent,respectively, the lower bound of the gap to optimal attained by a hybrid heuristicmixing the two is min{u, v}. The upper bound is not obvious to calculate. However,the numerical results (see ([2])) show that this gap stays reasonably low.

We note that, for the time being, the numerical tests are only performed for small instancesto facilitate a comparison with the optimal solution. The performance of heuristics shall betested for larger instance sets, as well. Since no comparison is possible in this case, it may beinteresting to compare the performance of individual or hybrid heuristics with that of somegreedy algorithms. Most of the time, the supervisors in charge of control of operations incross docks do not have sophisticated methods to help them in their work. We believe thatthe way they work may be simulated by some greedy algorithm which makes local decisions.

3.3.3 Other types of problems

The studies mentioned above consider the minimization of operational costs as the optimiza-tion criterion. In an exploratory study, we have modeled the cross dock scheduling problemusing some time related optimization criteria. This study is realized during the master’s thesisof Asma Talhi (co-supervised with J.P. Gayon) [Tal09]. In the case of minimization of themakespan, [CL09] has shown that the cross dock scheduling problem can be reduced to flow


shop scheduling problems with parallel machines and is NP-hard. We have formulated theproblem with other time related objective functions, such as the minimization of the sum ofcompletion times, the maximum delay, the sum of delays and the total number of delayedjobs, for the case of single shipping and receiving dock problems. The resulting MILP mod-els can only provide results for small instances. Some heuristic solutions are tested for themodel which minimizes the sum of completion times. However, numerical experiments arenot sufficient to draw significant conclusions.

In an other study, we have explored the dock assignment problem for an original objectivefunction which is the minimization of the congestion. This study is carried out during themaster’s thesis of Onur Ozturk (co-supervised with M.-L. Espinouse) [O08]. Usually, dockassignments are done to minimize the distance travelled by the pallets. However, this doesnot necessarily facilitate the operations inside the cross dock. Typically, models employingthis objective function generate solutions in which the pallets traffic in the central docks areincreased. This generates a congestion at the center of the cross dock and slows down theoperations. In this study, the congestion is modeled as overlappings of materials flow insidethe cross dock. The objective is hence to minimize the number of these overlappings.

A MILP model is proposed for the resolution ([7]). However, this model does not finda solution in reasonable time when the number of docks exceeds 8. We were expecting thisperformance since the dock assignment problem with this objective function is very similar tothe problem of minimum crossing number in graph theory, with additional constraints. Thisproblem is proved to be NP-hard by Garey and Johnson.

3.4 Major contributions

A certain number of technical contributions are already mentioned in section 3.3 such asthe best performing heuristics, the comparisons with optimal solutions, complexity of thealgorithms and so on. We will not return back to these technical points.

In this section, we would like to present a global view of our major contributions in thecross docking literature.

We have first started working on scheduling problems in cross docks in 2005. At that time,we could find many studies on strategic decision making. On the operational level, the studieswere also reported as early as 1992 on the dock assignment problems. This is quite logicalsince this type of problems is very similar to gate assignment problems in airports which arealso extensively studied since late eighties. The literature on the scheduling of operations incross docks, on the other hand, was a virgin domain in 2005. For instance, the earliest workdate back to 2007 with single receiving and single shipping dock problems ([4]), [BPL07] and[SC07]. This makes us part of the pioneers in the domain. Different types of problems whichwe have resolved since then are summarized in table 3.3.

The domain is in a ramp-up period. The articles published recently are good examples ofhow well we are accompanied by fellow researchers. If we were to compare our results withtheirs we will be distinguished on the following points:

• One of the common points of the existing literature on cross dock operations schedulingis that the full information on the contents of the trucks are assumed to be available.Furthermore, either the inbound trucks are assumed to be present at time zero or thearrival sequence is assumed to be known. Hence, only an off-line scheduling policy is


handled. In reality, the companies have much less information and control on the in-bound transportation than the outbound one ( [SL01]). Therefore, assumptions suchas full knowledge on the input sequence is difficult to justify. In the case of the simplecross dock environment we relaxed this assumption to evaluate the value of informationon the arrival sequence. The state-of-the-art on cross docking in destination to practi-tioners ( [Nap00], [Sch97]) insist on the importance of high performance informationsystems for a successful implementation of this technique. Building, maintaining andgathering data for complex information systems is a cost-bearing activity. It is fullyjustified as long as the information contained in these systems contributes to the opera-tional performance of the system which makes use of it. But, do we really need completeinformation on the arrival sequence for good operational performance? As we have seenwith the numerical results, in the case of a simple cross dock, we do not necessarily needfull knowledge on all arriving trucks. Distant information has very low value while theinformation on the next few trucks is enough to obtain near optimal solutions. We notethat, the method proposed can be extended to multi-dock environment under certainconditions, as described in ([5]).

• Another difference comes from the way we evaluate the objective function. In all of theprevious works, the objective function considers a time-related performance measure,such as the minimization of makespan, or total tardiness. This is rather classical inscheduling. In our work, we directly focus on operational costs related to temporarystorage of merchandise inside the cross dock and the costs related to preemption ofloading operations at the docks. We believe that this is an original way of looking atthe scheduling problems. By definition, pallets arriving to a cross dock, shall not stayin the facility for more than a day. Therefore, we consider time as a constraint andnot an objective. Typically, a sequence of operations for which all pallets haven’t leftthe cross dock at the end of the day is considered infeasible and is discarded from thesolution space.

• For application purposes, there is a need for studying the multi-dock cross docks. Ac-tually, there are only very few studies on multi-dock problems. As mentioned before,[MSG05] and [Boy09] propose solutions for special cases whereas [CS09] requires eitherinbound or outbound side to be a single dock. Our studies, ([1, 6, 3, 2]) contributemassively in this area. We have both optimal and heuristic solutions for multi-dockcross docks. Furthermore, we study an original context where preemption of loadingoperations (truck replacement) and temporary storage are explicitly modeled. Thesefeatures are either forbidden or not modeled explicitly in the existing literature on multi-dock scheduling problems. We note that in practice, temporary storage or preemptionof loading operations are solutions employed to increase the flexibility of cross dockingoperations.


I O Order of arrivals Unloading Storage Truck repl. resolution method

1 1 Fixed LIFO Not allowed Allowed KTNS rule

> 1 > 1 Fixed LIFO Not allowed Allowed KTNS rule

1 > 1 Fixed Flexible Not allowed Allowed Heuristics

1 1 Fixed LIFO Allowed Not allowed Graph

1 1 Fixed Flexible Allowed Not allowed Graph

1 1 Fixed LIFO Allowed Allowed Graph

1 1 Fixed Flexible Allowed Allowed Graph

1 1 Flexible Flexible Allowed Allowed Tabou Search

1 1 partially known Allowed Allowed Heuristics

1 1 Unknown Allowed Allowed Heuristics

> 1 > 1 Fixed LIFO Allowed Allowed DP and heuristics

Table 3.3: A summary of different problems studied

Bibliography

[1] Gulgun Alpan, Sylvain Bauchau, Rim Larbi, and Bernard Penz. Optimal operationsscheduling in a crossdock with multi strip and multi stack doors. In 38th InternationalConference on Computers and Industrial Engineering – CIE38, volume 2, pages 1168–1176, Beijing, China, 2008.

[2] Gulgun Alpan, Anne-Laure Ladier, Rim Larbi, and Bernard Penz. Heuristic solutionsfor transshipment problems in a multiple door cross docking warehouse. Computers andIndustrial Engineering, doi:10.1016/j.cie.2010.09.010, accepted, September 2010.

[3] Gulgun Alpan, Rim Larbi, and Bernard Penz. A bounded dynamic programming approachto schedule operations in a crossdocking platform. Computers and Industrial Engineering,doi:10.1016/j.cie.2010.08.012, accepted, August 2010.

[4] Rim Larbi, Gulgun Alpan, Pierre Baptiste, and Bernard Penz. Scheduling of transship-ment operations in a single strip and stack doors crossdock. In International Conferenceon Production Research – ICPR’07, pages CD–ROM, 10 pages, Valparaiso, Chile, 2007.

[5] Rim Larbi, Gulgun Alpan, Pierre Baptiste, and Bernard Penz. Scheduling cross dockingoperations under full, partial and no information on inbound arrivals. Computers andOperations Research, accepted, october 2010.

[6] Rim Larbi, Gulgun Alpan, and Bernard Penz. Scheduling transshipment operations ina multiple inbound and outbound door crossdock. In 39th international conference onComputers and Industrial Engineering – CIE39, pages CD–ROM, Troyes, France, july2009.

[7] Onur Ozturk, Gulgun Alpan, and Marie-Laure Espinouse. Minimisation des croisementsde flux dans une plateforme de crossdocking. In livre des resumes, Roadef 2009, 2009.

55

56 BIBLIOGRAPHY

Chapter 4

Ramp-up management of newproducts

In this chapter we present our work in this domain since 2007. These studies are conductedduring the masters theses of Laurence Benoıt [Ben07], Laurene Surbier [Sur07], Mostafa Tah-masebi [Tah10] and Emre Hesapdar [Hes10], all co-supervised with Eric Blanco. LaureneSurbier has continued in doctoral studies with us in the same domain [Sur10]. This doc-toral thesis is carried out in collaboration with SIEMENS via a CIFRE agreement. Relatedpublications are ([1, 3, 2, 5, 4]).

The subject of ramp-up has an inter-disciplinary aspect by nature. The collaborationwith Eric Blanco formed an enriching exchange during the supervision of this research work.The research domain of E. Blanco is the collaborative design while my research is on theoperations management. The ramp-up period in itself is seen as a hand-over of a new productfrom designers to operations managers. Hence, it was interesting to integrate both points ofview.


As we will see next through the literature review, the research problems are very muchindustrial context dependent in this domain. Therefore, we first give a quick overview of ourindustrial context.

4.1.1 Industrial Context

Our industrial partner is in the Transmission and Distribution division (T&D) of the SiemensGroup AG in France. Their production sites offer products and solutions in the high-voltagefield, such as High Voltage Direct Current (HVDC) transmission systems, substations, switchgears and transformers. The sector is operating in the B-to-B business. The final clients areindustrial sites, health care establishments, operators in energy sector such as EDF, etc.

The production is governed by the engineering-to-order strategy, since each product iscustomized according to customer requirements. Switch gears are complex products thatare parts of the high voltage equipment of national electrical networks. Due to the varietyof electrical networks and the customization proposed by Siemens, the diversity of the finalproducts is very high. Our industrial partner is a typical example of a low volume, high

57

58 CHAPTER 4. RAMP-UP

diversity (LVHV) industry. As mentioned in [MB01], a distinctive feature of low-volumeindustries is the need to manage different types of development projects. These includecontract projects where the product is developed for a customer’s special requirements. Theycould also be product development projects to develop a new product or improve an existingone, either for sale as a standard item or customized for individual requirements of customers.

Our investigations are carried out in two Siemens production plants: the Grenoble plantin France and the Berlin plant in Germany. Both are composed of a R&D center and amanufacturing unit.

4.1.2 Where do we stand in the literature?

Research in the domain of ramp-up dates back to late nineties. So we can consider it as ayoung research area of about 10 years old. We have made an extensive literature review onthese studies ([4]). One of the findings is that most of the studies were originated from anindustrial context. The studies are mostly concentrated in micro-electronics and automotiveindustries as seen in figure 4.1. This is quite understandable since innovation is a major driverin micro-electronics industry. In automobile industry, new models do not come out as often asin micro electronics, however, re-styling (i.e. changes in a part of an existing vehicle) appearsquite often. Therefore, the new product launch is highly experienced at the upstream supplychain actors in the automotive industry.

Another finding is that based on the context, the point of view, the tools or the per-formance measures of ramp-up projects may vary. For instance, the notion of learning inramp-up projects does not have the same dimension if the industry is labor-intensive orhighly automated. Similarly, the work pressure is not the same if the throughput is high (e.g.1 min 30 seconds per vehicle in an automobile assembly line) or low (e.g. several hours per1 high voltage switchgear). Therefore a best practice in an industrial context is not directlyapplicable in another industrial context.

Both automotive and micro-electronic industries are high production volume with mediumto high product diversity sectors. If we place the existing literature on a production volumeand product diversity plane, we observe that the industrial context of the existing work ishigh production volume and medium to high diversity product industries. The low volumeindustries with high product diversity are only covered by our research work (lower line infigure 4.1).

When we classify the existing literature (excluding our work) based on the problems onwhich they are focused we obtain table 4.1. As can be seen, problems related to the logisticsas well as the cooperation and coordination issues haven’t been addressed even though theyhave been highlighted as problems encountered by most of the case studies mentioned in table4.1. As we will describe next, our research work will focus on cooperation and coordinationissues to occupy this empty spot.

4.1.3 Research objectives

Based on the panorama given in section 4.1, our first research objective is to identify thetypical problems encountered during a ramp-up situation in a LVHV industry. Are the prob-lems encountered in a LVHV similar to those encountered in other well-documented industries(micro-electronics, automotive)? And finally, among the identified problems, which are themost problematic ones? In ([3]) we answer these questions.

4.1. RESEARCH OBJECTIVES AND OUR PLACE IN THE LITERATURE 59

Figure 4.1: Industrial context of research works published between 1998 and 2010 concerningramp-up [4]


Focus Area Problem Description Publications

Product insufficient product specifications, [MH06], [SRKLH07],[WC07]product changes,lack of product maturity,late engineering changes,

Technical maturity of the production processes [HPT03], [TY04], [JM05]processes Slow set-ups, unforeseen bottlenecks

product design-process fit

Logistics on-time availability and quality of [BSD99]components provided by external suppliers

Quality control quality of the end product [Alm99b], [Alm99c], [JM06][BT99], [FSL03], [BG06]

Methods and tools Methods and tools used for [Alm00], [VdM04], [FSL03]piloting the ramp-up phase [KH05],[WHN07]

Personnel ill definition of responsibilities, [BT01], [SJLM08], [SHFB08]lack of qualified personnel, [TY04], [JM06]Lack of training,

Cooperation lack of communication and cooperation, [Api03]information loss,trust on received information

Descriptive studies Case studies [SHR+98], [Alm99b], [DB99],[Alm00], [TBC01],[KWES02],[NW04], [SDT04], [CF06],[MH06], [SFB06], [SLJM06],[FSHS09]

Table 4.1: Classification according to focus area

As we will see in section 4.2, cooperation and coordination issues turn out to be pre-occupying in our industrial context as well. Hence our second reseach objective is to charac-terize the cooperation during ramp-up? We will use the notion of interfaces and informationexchange to this end. The goal is to develop methods which aid in identifying the criticalinteractions among the ramp-up project actors and to figure out where and how critical in-formation is exchanged. Using these tools, we shall be able to draw actionable conclusions toimprove future ramp-ups. The methods presented in ([1, 2, 5]) answer this research question.

4.2 Solution methods

The methods developed during the thesis of Laurene are based on case study approach. Onedifficulty of this approach is to remove the bias from the cases so that general conclusions canbe drawn. We have put a special emphasis on this issue in selecting the case studies. Thevalidity of a case study is evaluated based on the following criteria commonly used in casestudy research:

• The sampling: Cases are selected either because they predict similar results or theyproduce contrary results for predictable reasons.


• Reliability: reflects to which extent the case can be repeated with the same result. Inorder to increase the reliability, the case study protocol shall be documented with care.

• Construct validity: shows how well the operational measures were chosen for the issuesunder study. To increase construct validity, multiple sources of data can be used tostudy the same phenomenon and the results are to be validated by key informants.

• Internal validity: refers to the fact that the concept proposed as a hypothesis reallyexist.

• External validity: refers to the fact that the results obtained are valid beyond theimmediate case under study. This can be improved by replicating cases or payingattention to sampling process.

During the thesis of Laurene Surbier we were able to conduct case studies on 4 different newproduct development projects. The above validation rules are taken into consideration andare explained in detail in [Sur10]. Table 4.2 gives details on these projects:

Project A Project B Project C Project D

Project 06/06−06/07 11/04−12/08 03/06−05/09 07/08−04/10start−end

Type of Product New New Newproject transfer product product sub-system

Observation 04/07−06/07 11/07−04/08 10/08−11/08 10/09−01/10period

Project Run-up Preproduction Run-up PrototypingStatus run and Testing

Location Grenoble Grenoble Berlin Grenoble

Table 4.2: Case studies conducted during [Sur10]

As seen in table 4.2, the range of project maturity and the complexity covered by thecases are quite wide. In the literature, it has been shown that ramp-up of a new product ismore or less complex depending on the level of newness of the product and also the processes[Alm99a]. Figure 4.2 classifies the above projects based on this categorization.

Similarly, if we place the projects on a timeline, we see that the observation periods coverthe whole new product development process as described by [UE04] (see figure 4.3).

4.2.1 Major ramp-up problem types in a low volume industry

There is a consensus on the importance of discovering and removing bugs, problems, andmissed opportunities during the ramp-up phase. Terwiesch et. al. [TBC01] even define thisas the most important activity during the ramp-up period. If not taken care of, the occurredproblems may cause delays or scale down the project of a new product, resulting in majorloss of profits. Therefore, many case studies have been conducted to identify problems duringramp-up (see table 4.1). These case studies are in high volume industries context. In orderto identify problem types in a low volume industrial context, we have followed project Aduring its ramp-up period (see table 4.2 and figures 4.2 and 4.3 for details). Before April


Process

Product

Complexity

Project AProject C

Project D Project B

Existing Modified New

New

Modified

Existin

g

Figure 4.2: Classification of different projects

MATURE

PRODUCTION

RAMP−UPDEVELOPMENT

Prototyping and

testing

Pre−production

or pilot runRun−up Normal production

conditions

Project D Project A

Project C

Project B

OF

START

PRODUCTION

Figure 4.3: Maturity of the projects during the observation period

2007, product A was only produced in Berlin and in Shanghai. Faced to increasing demand,Grenoble plant was qualified as a new production site to increase the global production capac-ity. Project A is hence a product transfer project. Nevertheless, most of the characteristics ofa new product ramp-up was observable since the product has never been produced with thegiven process in Grenoble plant. We note that, since the product itself is a mature product weare not expecting to see problems related to product itself (i.e. problem with specifications,late design changes, etc.).

During the 3-months observation period daily research notes were taken, with the majorgoal of describing without judgment what was happening. Facts and major events wererecorded in a field research notebook. The details on how events are identified as problem(or not) and the procedures followed during the case study can be found in ([3]). At the endof the observation period, 107 problem statements were compiled. These problem statementswere then classified using two different classification methods.


Resource based classification

This classification is based on a method proposed by Harper and Rainer [HR00] for classifyingproblems in the technology transfer area. To create a logical classification (easy to understandand to reproduce), the authors state several guidelines to follow. The most important one isto determine top-level categories by a problem attribute present in each problem statement.In their study, they identify the physical or non-physical resource as a general attribute ofthe collected problem statements. This procedure results in the classes illustrated in figure4.4 for the 107 problem statements.

Figure 4.4: Classification of 107 problem statements based on resources ([3])

We observe in figure 4.4 that most of the problems are related to the physical flow ofcomponents. Furthermore, the sub-classes of the components class show that most of theproblems are related to minor materials flow. In figure 4.4 , 1st flow components refer to themajor materials flow such as the class A items in an ABC classification (or the Pareto curve)while 2nd flow class refers to C-type items. Of course, having many problems related to Cclass items does not mean that these problems were the most impacting ones. To completethe study, we performed a risk analysis on the components class problems. This analysisconfirmed that C class items were actually not only the most common but also the mostimpacting ones.


This is an interesting finding which shows that during the ramp-up period using classi-cal production planning techniques do not necessarily perform well. For a mature productenvironment, ABC classification of components and deploying more-or-less elaborate man-agement strategies of materials flow based on this classification is a rule of thumb. In ramp-upenvironment, however, every item has its importance and the project may suffer even fromproblems related to the most insignificant components, such as bolts and nuts.

When we compare our results with other case study results, we observe that the problemzones identified here are very similar to those identified earlier in automotive industries (see[Alm99a, NW04, FSHS09]). In micro-electronics industry however (see [TBC01]) materialsflow has not been reported as a problematic issue. We believe that this is due to the fact thatthis industry is more of a process industry than a manufacturing one. The focus is for themost part laid on the efficiency of the production processes.

Unlike our results however, these early works do not provide any numerical values or riskanalysis to rank the relative importance of the problematic areas which makes it difficult tomake comparisons between high and low volume industries.

Origin based classification

The drawback of the resource-based classification is that we observe where the problem is butwe do not see why it occurs. To this end, we made a second classification based on the sourcesof the problems. The classification procedure is given in ([3]). Here we give the final resultin figure 4.5.

In figure 4.5, we observe that cooperation and information exchange were major sourcesof problems during a ramp-up situation. In two case studies [MH06] [NW04] communicationproblems were also identified as a cause of failures. However, only a few studies exit toovercome communication problems (see table 4.1)

As a consequence, were were eager to further investigate the communication issues asmuch as the practitioners did at Siemens T&D.

4.2.2 Auditing tools to characterize interfaces

Communication and information exchange problems occur when different people or entitieshave to interact together to achieve a common goal. In our case the goal is a successfulramp-up. As we have seen in the previous section, the root-cause of many problems was thelack of communication.

In order to understand how different actors involved in a ramp-up phase interact and toidentify the weaknesses (and also the strengths) of these interactions, we proposed a series oftools based on the notion of interfaces.

An interface is the collection of links and interactions existing at the boundary of differentindustrial functions (e.g. R&D, Production, Procurement, Supply Chain, Human resources,...etc.) and they support communication and coordination.

In the literature some models already exist to describe interfaces among industrial func-tions. The activity model used in the domain of management science [Eng00] can describemediating artefacts, rules, community and work division between a single actor and a singleobject or task. Analysis on multiple actors is however too time-consuming. Information flowanalysis framework proposed in [FS01] focuses on information flows, processes and operatingperformance and gives a precise map of information flow and their characteristics. However,


Figure 4.5: Classification of 107 problem statements based on problem-source ([3])

the actors and their behaviour is omitted from the model. Finally, the project actors interfacemodel proposed by [KPB04] detail an interface situation in five distinctive elements, namelythe interface actors, the intermediary objects, the tools, the procedures and rules and theinterface spaces and times (See figure 4.6). In this case the actors, the information and theoverall context is explicitly considered however the information dynamics (information matu-rity, evolution) is not taken care of. In the case of a new product, the information is highlydynamic and evolves over time. It is hence important to capture this dynamic behavior.

None of the above methods was completely appropriate for our case. Nevertheless, themethod by Koike et. al. [KPB04] had some interesting features. Therefore, in order torepresent how different project actors interact during the ramp-up phase we have proposedan interface model inspired by [KPB04]. In this model we use the following notions developedin [KPB04]:

• interface actors are persons or groups having an interest in the project.

• intermediary objects, (IO), are originally developed by Jeantet and Vinck [JV95]. Theseare the items that are used or created during the design process and are used to exchangeinformation among actors.

• tools are different ways of exchanging information for a given interface.

• interface spaces and times are moments and places where stake holders can interact


Figure 4.6: Elements of project actors interface model proposed by Koike et. al. [KPB04]

during the project. They are dedicated moments and places to create or use interme-diary objects. The interface times could be either synchronous (such as project statusmeetings) or asynchronous (such as e-mail exchanges).

In order to provide a more precise description of the information exchanged the followingattributes are added to Keiko et. al.’s model. We note that we have chosen these attributes(and not some others) since they are easily retrievable during a case study and are diverseenough to englobe the information exchanged:

• Information characteristics: In our model, information will be carried out by the IOand we need to identify the characteristics of the exchanged information (for example,if contextual elements are given or if the data is more or less precise). The informationstructure, a concept developed by Gardoni et al. [GFV05], is helpful in this characteri-zation. According to the authors information can be structured in 3 distinct levels. Aninformation is said to be structured (denoted SI) if its contents and form are stronglyregulated and fixed through rules and procedures. For example, a design drawing is anIO with Structured Information. That is, all information enclosed in the drawing sheetis mandatory and thoroughly predefined by official company rules. An information issaid to be semi-structured (denoted SSI) if its content and the form can only be par-tially shaped by the company’s official rules. For example, minutes of meeting couldalways be handed out following the same frame but the content will differ. SSI is ei-ther very explicit or totally meaningless for external actors, depending on their personal


knowledge. Finally, a piece of information is said to be non structured (denoted NSI)if it is not formalized. Context elements are the bare minimum for the informationreceiver to understand. Another attribute to characterize information is the supportwhich refers to the means by which the information is exchanged (e.g. a sheet of paper,e-mail, software output, etc.).

• Information dynamics: As we mentioned before, the context of ramp-up (and so is theinformation) is extremely dynamic by nature and is governed by many uncertainties.To capture the dynamic nature of information, the following indicators are used.

– Update frequency: The more frequent the changes are, the more dynamic the in-formation is.

– Evolution: When a new product is introduced in a production system, some ex-changed information may not be mature as is the case for the product itself. Usu-ally, R&D and production departments (or some other support departments likethe procurement) do not work in a sequential way. Some of the activities overlap.Hence, information exchanged at time t may be subject to changes at time t + δ.This is why [KEW97] focus on the degree of evolution of information. The degreeof evolution is valuated by a function which describes if the information evolves ina fast or a slow manner. Information with a fast (resp. slow) evolution will quickly(resp. slowly) reach its final value. Only minor (resp. major) changes will occurafter the first delivery of the information.

– Openness: This notion refers to if someone other than the source of informationcan make changes on the information. If the answer is yes we say that the objectwhich carries the piece of information is open, otherwise it is closed

• Impact of information: to evaluate the impact of information, we use the sensitivityof downstream activities (see [KEW97]) which measures the impact of information onthe tasks and the update duration which measures the impact of the information onthe actors. Sensitivity of an activity is valuated by the rework cost needed in theseactivities to take into account the information evolution. Update duration is valuatedby the effort of an actor to update an information (e.g. a few seconds, a few hours, etc).

• Information dissemination level: captures where the information is exchanged. We willuse 3 out of 4 workspaces defined by [BGR07]: The public workspace is where the officialdeliverables are published internally or externally (e.g. with customers or suppliers).Hence, the information exchanged is expected to be extremely formalized. The projectworkspace is where the project team members exchange information. This level is stillinfluenced by the company rules for formalizing the information. Finally, the proximityworkspace corresponds to information producer’s personal network. The actors whichare accepted in this network can profit from informal information exchange.

We added the above attributes to Keiko’s et. al.’s model. Based on this enhanced interfacemodel, we proposed two auditing tools :

• The IO Grid: investigates different characteristics of exchanged information andproject structure through the listing of the project intermediary objects.


• The SIT Grid: investigates the major information flows using the notion of syn-chronous interface times (SIT) that is where and when the information is exchanged.

Finally, we propose a summary diagram to give a global picture of the interface for agiven project. Details on these auditing tools can be found in ([1, 5]).

Figure 4.7 gives a global view of the two auditing tools and the notions which are coveredby each one of them.

Figure 4.7: Elements of our interface model proposed in ([5])

The auditing tools provide a structured and easy to implement guidelines to describe andevaluate the interactions among a project’s actors. The weaknesses in the interfaces can beidentified by incompatible information. For example, if the information in a given IO issensitive, open with many users and yet non structured, this may generate some problems:Since many users can update it (open IO) in anyway they like (non-structured), informationmay be deformed and misunderstood by some of the users leading to wrong decisions. Sincethe information is considered sensitive the damage can be high. Hence, this IO needs to besecured.

We have tested the auditing tools on 3 different case studies. The concerned projects areproject B, C and D. Details on these case studies are available in ([1]), ([2]) and [Sur10].

4.3. ADDITIONAL STUDIES ON NEWPRODUCT INTRODUCTION ANDRAMP-UPMANAGEMENT69

4.3 Additional studies on new product introduction and ramp-up management

In the previous sections, we have presented the studies conducted in collaboration withLaurene Surbier and Eric Blanco. Some additional studies are carried out as masters re-search projects, (see [Ben07] and [Tah10]). Next we will give an overview of these studies.

Key performance indicators (KPI) play an important role in the management of a newproduct projects. The objective of the master’s thesis of Laurence Benoıt [Ben07] was toinvestigate the type of KPI the local companies use to manage the new product developmentprojects. To this end, a survey has been conducted with 10 participants. These participantswere some small and medium size companies in Rhone Alpes Region. They produce hightechnological products and face the ramp-up periodregularly. The findings of the survey wasquite interesting. First of all, companies have reported numerous difficulties during theirramp-up phases and highlighted that they do not have efficient management tools to controlthe launch. When they are asked on KPI followed to measure the performance of the newproduct projects, only 3 out of 10 responded that they have established a set of KPI. However,only 1 among 3 was really able to use them for the evaluation of their projects. This lattercompany has reported the lowest number of problems during the ramp-up phase. Therefore animmediate conclusion is that following some KPIs during the production launch can improveits performance.

This study as well as our literature review on ramp-up management has shown that theKPI may vary from one industry to another ([4]). For example, in micro electronics industryyield (i.e. percentage of final products which are not scrapped) is the major KPI. The endof a ramp-up phase is defined by a target yield. However, in low volume industries yield ascalculated in micro-electronics do not have sense since nothing is scrapped. This KPI will beexpressed as amount of rework or rate of first-time-quality in a low-volume industry. Eventhough interpretation may be different depending on the industrial context, four major classesof KPI are reported:

• Cost related KPI: usually expressed in terms of extra amount of resource or manpowerused to make up the capacity loss, or extra inspection and corrections.

• Quality related KPI: Yield, rework, rate of first-time-quality

• Quantity related KPI: capacity utilization (e.g. number of produced products/numberof planned products), amount of WIP.

• Time related KPI: production cycle time, time between project milestones and time-to-market.

The KPI given above are good starting points to manage the ramp-up. However, theyshould be adapted to each industrial context. Furthermore, we believe that it is not enough tofollow up individual KPI but to understand the trade-off between various KPI. It is obviousthat cost/quality and time related KPI are interconnected. However, the degree with whichthey are interconnected is interesting to investigate. The masters research project of MostafaTahmasebi is an initial investigation in this direction [Tah10]. In this project, the objectivewas to observe the effects of different ramp-up management strategies on the KPI of thesystem. Here, the amount of rework and the throughput of the system are taken as the KPI.


To this end two simulation models were developed for a pull and a push manufacturing system.The simulation results have shown that a ramp-up management strategy which improves oneof the KPI is not necessarily well performing for another KPI. For instance, if we choosea ramp-up strategy which forces to reach a target throughput objective in a shortest timeperiod, the amount of rework during the ramp-up period exhibits a sinusoidal behavior givingsigns for unstable product quality. If the product quality is primordial for the company, thistype of ramp-up management strategy will not be convenient.


We can cite several contributions in this domain:Earlier studies in the domain are all oriented towards high volume industries. Our research

is conducted in a LVHV context. Our problem classifications as well as audit of differentramp-up projects have highlighted several vulnerable points in these types of industries.

• One of the weak points is the difficulties observed in the parts’ procurement. Procure-ment problems during ramp-up are recorded in high volume industries as well. However,percentage of such problems are never mentioned in these previous studies. We havesome reasons to believe that the type of procurement problems are quite different in thecase of LVHV industries: First of all, diversity is very high, therefore, several variants ofeach component are needed. This renders the setting-up of the upstream supply chainmore difficult. Secondly, low volume induces weak materials flow from suppliers to pro-ducers. Hence, the low volume industries have less power over their suppliers than thehigh volume industries. This means that the supply process is subject to higher varia-tions since the suppliers do not consider deliveries of small quantities with an utmostimportance. Finally, low volume industries usually produce high tech products withhigh quality requirements. Hence, supplier qualification takes longer.

• Another difficulty arises due to the structure of the ramp-up phase in the LVHV in-dustries. The pre-series production observed commonly in high volume industries areskipped in the case of LVHV context for several reasons: High variability of productsmakes it difficult to design a relevant pre-series production. Furthermore, the final prod-ucts are often very expensive. Pre-series are produced without having a real demand.The cost of the final product discourages LVHV industries from pre-series production.We recall that preseries are used to debug the production process of a new product.Without pre-series production, the production line is subject to a higher number ofdiscrepencies.

• The management of ramp-up in LVHV industries is organized in a project mode. Thismode requires an important coordination among the project actors.

We provided some methods related to this final point. We have proposed a set of auditingtools to characterize the interactions among ramp-up project actors. This tool is easy to useand is proved to be efficient to highlight problem areas in coordination. Our case studies werein the domain of new product launch. However, the tools are generic enough to be used inother context of project management.

From a practical point of view, the identification of problem types enabled our industrialpartner to be aware of typical problems and take action for future ramp-up projects. For


instance, the identification of the problems related to the secondary supply process resultedin allocation of additional resources to control the supply process in up-coming ramp-upprojects. Similarly, the results of the second classification encouraged the company to launchan improvement program on the information exchange during New Product Developmentprojects. The auditing tools helped the company to have a benchmark on their collaborativetasks during the production ramp-up. Even though, all production units are ruled with acommon company policy in theory, the case studies highlighted very different collaborationpatterns. Indeed, the individuals seem to interpret and apply the common company policiesdifferently. We were also able to establish a relationship between the collaboration patternsand the problems encountered. This final result enabled the company to identify best practicesinside the group and take action for future ramp-up projects.

Bibliography

[1] Laurene Surbier, Gulgun Alpan, and Eric Blanco. Audit of production launch of a newproduct in a multinational company. In Proceedings of the 42nd CIRP Conference onManufacturing Systems, pages CD–ROM, Grenoble, France, Juin 2009.

[2] Laurene Surbier, Gulgun Alpan, and Eric Blanco. Contribution of two diagnosis tools tosupport interface during production launch. In Proceedings of the CIRP Design Confer-ence, pages 331–337, Cranfield, may 2009.

[3] Laurene Surbier, Gulgun Alpan, and Eric Blanco. Identification of problem types dur-ing production ramp-up. In Proceedings of the IESM conference 2009, pages CD–ROM,Montreal, Canada, may 2009.

[4] Laurene Surbier, Gulgun Alpan, and Eric Blanco. A comparative study on productionramp-up: state-of-the-art and new challenges. submitted to International Journal of Pro-duction Economics, 2010.

[5] Laurene Surbier, Gulgun Alpan, and Eric Blanco. Interface modeling and analysis duringproduction ramp-up. CIRP Journal of Manufacturing Science and Technology, 2:247–254,2010.

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74 BIBLIOGRAPHY

Chapter 5

Petri net modeling for riskmanagement in supply chains

In this chapter we present our research on risk management in supply chain networks. Ourinvestigations in this domain started in late 2006 by the collaboration with Gonca Tuncel fromUniversity of Dokuz Eylul, Turquie. Our initial research findings are enriched in collaborationVan-Dat Cung and Fabien Mangione. This chapter will cover the results obtained throughoutthese collaborations ([2, 3, 1]).


In the literature we find various definitions of risk. Here, we take the following definitiongiven by the association for Project Management since it highlights several notions, namely,objectives, occurrences and impacts. We can find natural correspondences for these notionsin supply chains. Risk is defined as an uncertain event or a set of circumstances which, shouldit occur, would have an effect on the achievement of one or more objectives [fPM04].

A typical process of risk management contains four basic steps [HVT02]. This procedureseems to receive consensus in the literature, and is applicable to risk management in supply-chain processes as well:

1. Risk identification: This first step helps in developing a common understanding of thefuture uncertainties surrounding the supply chain, thus recognizing potential risks inorder to manage these scenarios effectively.

2. Risk assessment: Refers to the assignment of probabilities to risk-bearing events in thesystem defined in the first step. The consequences of these risk events are also identifiedin this step. Associating probabilities to risks is not an easy task and requires tediouswork. A company’s own experiences, benchmarking on other companies’ performanceresults or forecasting analysis can be used to this end.

3. Risk-management actions: In [Mus06], risk-management actions are classified as risktaking, risk mitigation, risk avoidance and risk transfer. We can also classify them asreactive or proactive actions. For instance, the integration of back-up scenarios shoulda pre-identified risk actually occurs is considered as a reactive action, while the riskmitigation actions which act directly on the pre-identified risks in order to reduce either

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76 CHAPTER 5. SUPPLY CHAINS RISK MANAGEMENT

the occurrence probability or the degree of severity of its consequences are typicallyproactive.

4. Risk monitoring: is the final step of a classical risk management process where thesystem is supervised to detect the risks when they occur.

Even though there is a consensus on the above procedure, a wide range of risk analysismethodologies exist for implementing the above steps in an industrial environment. 62 anal-ysis tools are reported by Tixier et. al. in process industries [TDSG02]. The number andnature of these methods are signs for complexity and diversity of the problem.

Our objective within the context of the supply chain (SC) management is to model andmitigate the operational risks for a whole supply chain. Risk management in supply chainsis a relatively new topic. In the literature, supply-chain management, performance analysis,and risk management (RM) have generally been considered separately. Risks are mostlyaddressed from the financial or economic perspective [WK06]. Although some recent literaturetackles risk management from the logistics point of view, these studies often look at a singleorganization’s vulnerabilities [J05] and tend to focus on a single point of view such as riskof unstable supply or demand, and product or information management [Tan06]. Thesemethods are difficult to extend on several organizations or are different enough so that nointeroperability is possible between them to observe the combined effects of several disruptionsat the same time. Therefore, we were interested in a global method for modeling and analyzingseveral operational disruptions at the same time.

Such a modeling paradigm is provided by Petri nets (PN). Petri nets are graphical andmathematical modeling tools. A PN is a directed bipartite graph, with two types of nodes:Places denoted by circles and transitions denoted by bars. Places and transitions are used tomodel the physical structure of discrete event systems. Dynamics of the systems are modeledvia tokens, which are illustrated by black dots which can circulate inside the physical structureand indicate the state of the system depending on their distribution over the set of places.Formally a PN is expressed by a 5-tuple (P, T,O, I, µ0) where:

P is a finite set of places.

T is a finite set of transitions.

I : P × T → N is the set of input arcs connecting places to transitions.

O : T × P → N is the set of output arcs connecting transitions to places.

µ0 is a vector of size P and represents the initial state of the system.

The interested reader can refer to [Mur89] for more technical details and [DAJ95] for theirapplications in manufacturing.

The reasons for choosing Petri nets as a base model are numerous:

1. Some typical behaviors of supply-chain networks such as concurrent and asynchronousactivities, multi-layer resource-sharing, routing flexibility, limited buffers, and prece-dence constraints, can be explicitly and concisely modeled by PNs.

2. Several extensions of the basic PN formalisms allow for the modeling of various notionssuch as time (e.g. Time Petri nets), stochastic behavior of the system (e.g. StochasticPetri nets), complex structured data, and algebraic expressions to annotate net elements(e.g. high-level Petri nets such as colored Petri nets (CPN)).


3. They support modular modeling techniques and are therefore handy in extending simplemodels into generic ones.

4. The Petri net models can be computerized using existing PN modeling tools and henceenable simulation for decision making.

5. Finally, unlike earlier work reported on risk management [Tan06], the PN models permitan easy interface to model and analyze several disruptions (disruptions in demand,transportation, quality, etc.) at the same time and on the same model.

For all of these good reasons, PN is being applied on various supply chain applicationsfor the last decade. However, these early studies do not cover how Petri nets can be usedto analyze and mitigate risks in supply chains. A recent survey by Zhang et. al. gives anoverview of the PN applications in supply chains [ZLW10]. However, these applications donot cover risk management.

In our studies we purpose an extension of Petri nets applications to the risk managementin supply chain networks. We give an overview of our methods in the next section.


As an initial step we considered a generic SC structure ([2]). This base model is presentedin figure 5.1 where the central SC actor is the manufacturer. For illustration purposes, thenumber of suppliers is limited to two. Similarly, only immediate suppliers and customers areconsidered. We note that, the model can easily be extended to more suppliers and higherlevels of complexity by including the suppliers of suppliers and the customers of customers,etc.

Figure 5.1: A generic supply chain structure

5.2.1 A high level Petri net based model

Our initial objective was to test the capability of Petri nets to model operational risks insupply chains and impact of risk mitigation on the global performance of a supply chain. In([2]), we worked on a hypothetical setting described below:

1. The materials and information flow: The SC structure given in figure 5.1 is taken asa reference model. Lean manufacturing tools being very popular in many industrialcontext, the relationship between the supply chain partners is considered to be a pull


policy with some safety stocks. That is, the downstream actor places an order, ifthe order can be fulfilled from the inventory, the products are delivered immediately,otherwise the production starts. Order arrivals are random.

2. A set of disruptions are considered as operational risks:

(a) Quality problems arising from processed products: We assumed that quality prob-lems may appear either at the suppliers or the manufacturer. If possible, they areresolved via reworking, otherwise the parts are scrapped.

(b) Technical problems and machine breakdowns: We modeled these disruptions onlyfor the manufacturer.

(c) Uncertainty in customer demand.

(d) Disruptions in the inbound/outbound logistics such as damages or loss of a ship-ment due to accidents, or delayed arrival of a vehicle because of heavy traffic,etc.

3. Performance measures: In ([2]), only the performance measures related to customersatisfaction is considered (e.g. customer order fill rate, proportion of orders cancelledor delayed, average tardiness (or earliness) of orders, total cost of delayed or cancelledorders.). We note that, it is very easy to extend the model to other system indicatorsrelated to storage or transportation since all material flows are kept track of via thetoken movements.

4. Risk mitigation actions: As mentioned before, risk mitigation actions act directly onthe pre-identified risks in order to reduce either the occurrence probability or the degreeof severity of its consequences. Here, we considered that a mitigation action will reducethe probability of a risk on a task, but the process will be charged with additional cost.For instance, to reduce the risk of disruptions due to absenteeism, we may put in placean incentive program. It is estimated that there will be less absenteeism but this willcost some money. Less absenteeism will impact many other performance measures inthe system. The global impact has to be calculated in order to know if it worths puttingin place such a mitigation action.

For modeling, we have used colored petri nets (CPN) because this type of PN combinesthe simplicity of representation provided by an ordinary Petri net and the power of high-level programming languages to manipulate complex data structures. Indeed, the modelercan attach piece of computer programs on tokens or transitions to control the execution oftransitions or to specify token behavior, etc. The major steps involved in the construction ofthe CPN is as follows:

The structure of the basic SC network with the required materials and information flowsis represented in a PN formalism. This model has three different types of transitions:

• Transitions illustrated by empty rectangles function in the same way as classical Petrinet transitions. They receive tokens from their upstream places and once the firing timeis over, they deposit tokens into their downstream places. Durations such as machiningor transportation as well as the order arrival times are modeled by stochastic timedtransitions.


• Transitions with a thick line on the side are called action transitions. These can changethe data/attributes attached to a token. For instance, the due date tolerance of anorder can be calculated within an action transition and a new due date can be assignedto an order (modeled by a token).

• Transitions with a black diamond are predicate transitions. They can be fired only ifcertain conditions are satisfied. Hence, they include some if-then rules. For instance, ifthere are no system failures, then the manufacturer can continue to produce.

We then model the set of disruptions by modifying the initial model with additional transitionscharacterized by a probability of occurrence. For example, during the transportation of afulfilled order, some of the products may be damaged (we assign a probability of occurrence).In this case, the products can be reworked or scrapped and reordered. To model this, newtransitions and places are added to the model. Status of the system can be checked regularlyat some transitions and the performance measures can be updated using simple programsattached to the model. Finally, mitigation actions are incorporated as if-then rules to changethe occurrence probabilities of disruptive events and to update the cost function.

The proposed CPN model can be seen in ([2]). Artifex PN software tool (RSoft DesignGroup, Inc. 2002) is employed to model the system. Artifex exploits a graphical language,which is based on extended Petri Nets, can define detailed models of a system or process, andcan be coupled with standard programming languages like C and C++.

This CPN model can be used for simulating the system behavior. The simulation resultscan be used:

• To identify the disruptions with the highest impact on the performance measures.

• To test different mitigation actions and to evaluate their impact on the global perfor-mance of the system.

• To calculate the cost that we are willing to pay to implement a given mitigation action.

5.2.2 Case study

In ([2]), we have started from a hypothetical, yet generic, industrial supply chain to construct aPetri net model which incorporates the risk management. Different simulation runs performedin this study has shown that managing risks with some mitigation actions may be beneficialfor the overall supply chain performance measures (in terms of customer satisfaction).

The limits of this study is the fact that the type of disruptive factors, their occurrenceprobabilities, their impacts, etc. are assumed. If we refer back to the basic steps of the riskmanagement, we can say that the risk identification and the risk assessment steps are partlyignored. Hence, one may ask the validity of the model in an industrial context. In order tovalidate the applicability of our methodology, we conducted a case study in a medium sizecompany in Turkey. The company produces components for electric, automotive, and homeappliance industries. The company procures raw parts from its suppliers, while it is also asupplier of his own customers as in figure 5.1.

To conduct the case, we have followed the major steps of the risk management which isexplained in section 5.1.

For the risk identification, we have used the Failure Mode Effects and Criticality Anal-ysis (FMECA) [Dep80]. This is a well-known technique commonly used in industry for risk


identification and assessment. In order to identify the major risks (i.e. the possible failuremodes) inherent to the supply chain of the company as well as the possible causes, we haveconducted a series of interviews with experts and cross functional teams in the company andat their suppliers. The FMECA method requires three measures to be collected:

• Si, severity index: Used to classify the relative importance of the effects due to a failuremode i.

• Oi, occurrence probability of failure mode i

• Di, detection difficulty of failure mode i.

In our case engineering judgments and historical records stored in databases were used todetermine Si. The occurrence probabilities, Oi, are extracted from statistical data sourcesabout the process such as the quality control results, monthly reports on traffic monitoring,insurance statistics, and daily operator performance evaluation results. Di is mostly basedon expert opinion. Si, Oi and Di are expressed as a value on a 1-10 scale. The higher theassigned number is, the higher the severity of the impact, the occurrence probability and thedifficulty to detect the failure is.

A risk priority number (RPNi) is then calculated for each failure mode as in equation5.1. RPNi draws the system analysts or supply chain managers attention towards the mostcritical activities.

RPNi = Si ×Oi ×Di∀i (5.1)

The results of this group of interviews and data collection can be found in ([3]). Using theFMECA method we have determined fourteen common supply chain disruptions (or failuremodes). We observe that most of these failure modes have already been considered in ourinitial model. Therefore, the global structure of the CPN model did not require major changes.We mostly updated the input data sets. Since real data was collected, we were able to feed themodel with the real occurrence probabilities or cost data. Furthermore, during the interviewswe could also list possible action plans. Hence possible mitigation actions can also be foundin the FMECA table. For instance, in the case of the failure mode due to the machine breakdowns, insufficient maintenance is shown as a potential cause and periodic maintenance isproposed as a mitigation action. Cost of this mitigation action can be estimated.

The CPN model is then simulated with the company’s data. The complete experimentaldesign, simulation parameters set-up and the simulation results can be found in ([3]). Here,we will only highlight some important findings:

• We were able to test that the risk management via mitigation actions can have a positiveeffect on the performance of the company. The customer order fill rates and total revenueincreases as mitigation actions are engaged. However, as we multiply mitigation actionsto reduce the risk occurrences, systems’ costs start climbing and a trade-off betweenaction costs and objective performance measures shall be found. Via simulation, ourmodel may assist the decision maker in this trade-off.

• The model can identify the risk category with the highest impact. For instance, forthis case study the quality problems turned out to be the most impacting ones. Eventhough the technical problems have a very high risk priority number in the FMECAtable, mitigation actions in this area had much less impact on the performance measurescompared to disruptions in product quality.


• The model is used to test individual mitigation action scenarios and find the mostimpacting and the least costly ones. This feature is interesting for a decision makersince budget is often limited and he would like to make the best out of it.

5.2.3 Object Oriented Design approach for PN modeling

Even though the structure of our CPN model did not require a lot of changes to conductthe case study mentioned above, we believe that this initial model is not modular enough toanalyze larger systems. To overcome this disadvantage, we have applied the Object OrientedDesign (OOD) approach in [1]. The idea here is to make use of this approach to have amodular structure for our risk management model. We have made use of the Object ModelingTechnique diagram (OMT) to find objects in the SC and to model the relations among them.In figure 5.2 we see OMT diagram of the supply chain of figure 5.1. The attributes of objectsare omitted here to simplify the representation. The OMT diagram is used to explicitlyrepresent different kinds of static relations such as generalization, aggregation, and associationamong the objects in the SC network.

Supply chain

network

RetailerSupplier Manufacturer Distributor

Quality

Control

Shop

FloorStorage

Maintenance

Customer1 Customer2

Outbound

Logistics

Custmr n

Inbound

Logistics

Supplier 1 Supplier 2 Supplier n

Generalization

"a kind of"

Generalization

"a kind of"

Aggregation

"a part of"

AggregationAggregation

"a part of" "a part of"

Generalization

Raw Materials

Storage

Finished Goods

Storage

"a kind of"

Figure 5.2: OMT diagram of the supply chain network

In figure 5.2, we have 4 super-classes: the supplier class, the manufacturer class, theretailer class and the distributor class. Each one of these classes are modeled as separate CPNobjects. These super-classes permit easy multiplication of supply chain actors. Attributes ofeach object which is a member of a given class can be assigned separately. For instance, if thereare n suppliers within the supplier class, internal attributes (e.g. n, production lead timesof each supplier, type of products that they can produce, etc.) can be assigned separately.Part/product flows in the OMT diagram are transformed into token color classes in the CPNmodel. The interconnection between the CPN objects are performed via a new set of places,called the input and output places. The CPN objects can hence be integrated into a singlesystem model by linking the input and output places of each CPN object. The CPN objectof the supplier class is given as an example in figure 5.3. The input places (illustrated by adouble circle) andoutput places (illustrated by a triangle inside a circle) are used to model the


external data/attributes which will serve as an interface to parts flow and information, andcontrol the flow between SC objects. All the related PN class models are connected throughthese such places modeled in each class. For example, the input place M ORDER receives theorders from the manufacturer. There is a corresponding output place in the CPN object ofthe manufacturer class which sends the orders. Similarly, the output place S PARTS deliversthe finished parts and there is corresponding input place at the manufacturer which receivesthe tokens. The black diamond on the corner of the CPN objects is called the port set. Eachport set is an ordered set of input/output places and is used to match the output places andthe input places of different classes.

Figure 5.3: CPN model of the Supplier class

Detailed description of each CPN objects can be found in ([1]).


Transition name Description

S1 Starting of inventory replenishment at the supplier

S OPR Processing by the supplier

SQ Receiving parts which have no quality problems.

SQ R1 Receiving parts which have quality problems.

S RW Reworking parts.

SQ R2 Rejecting the parts which have unsolvablequality failure.

S PCK Pick/Packaging the ready parts.

ST Transportation of the parts from the supplierto the manufacturer

ST R Transportation risk between the supplierand the manufacturer

ST RS Resolving/backing up the transportation failure

ST D Damage/ loss of freight

Table 5.1: Description of transitions for the supplier class

5.2.4 Coordination mechanism as a risk mitigation among supply chainpartners

In ([1]), the generated CPN objects are used with a new application. The objective of thisapplication was to test some coordination mechanism among supply-chain partners as a risk-mitigation action. A supplier selection mechanism to enhance the purchasing policy of themanufacturer is chosen to this end. This coordination mechanism is originally proposed byChan and Chan [CC06] as a Buyer-Vendor coordination. The Buyer (manufacturer in ourcase) has n Vendors (suppliers in our case). At each decision period, the manufacturer hasto decide on the quantity, the due date of order and the choice of the supplier for each orderso that the overall supply chain costs are minimized. The coordination mechanism works asfollows:

1. The manufacturer announces a range of product quantities [Qmin, Qmax] that he needs.

2. Each supplier submits a bid considering his own production capacity reservation for thisdemand and the possible start time. The bid consists of a delivery due date in responseto the manufacturer’s request.

3. The manufacturer selects the supplier from whom he will procure components. A con-tract between the manufacturer and the selected supplier is then signed.

4. Production process starts at the supply chain partners

5. If the selected supplier can ship what has been contracted so that the required quantityis delivered on time the coordination is completed.

6. If the shipment is not made on time, the supplier is penalized, depending on quantityand/or delay, as defined in the contract.


Chan and Chan uses the earliest delivery due date as the criterion for the supplier selec-tion mechanism described above [CC06]. This supplier selection step has been extended byMangione et. al. using a multi-criteria method which includes the historical data on suppli-ers in the selection process. This multi-criteria selection mechanism is incorporated into ourmodel to minimize the operational risks for the manufacturer.

To this end, additional input-output places are added to the supplier and manufacturerclasses to handle the bidding process. The supplier selection is not generated as a CPN class,since this module already exists as a Visual Basic program and provided by F. Mangioneand V.D. Cung. The CPN objects communicate with this program via an Excel sheet.It is possible to write bidding information at the beginning of the production period andagreement fulfillment information such as product delivery date or penalties paid at the endof the production period. Similarly, the supplier CPN objects will read from the Excel sheetto know if they are selected.


We believe that the original contribution of our research on risk management on supply chainsis the use of Petri nets which has not been tested before in this domain.

Throughout various applications we have shown many advantages of this modeling paradigmfor risk management in supply chains. We will recall the most valuable ones here:

• Unlike the previous work reported by Tang [Tan06], the PN model permits an easy inter-face to model and analyze several disruptions (disruptions in demand, transportation,quality, etc.) at the same time on the same model.

• PN models provide us a computerized support for decision making through tracking ofmaterial and information flow in the SC network by maintaining current status infor-mation of the entire system, and properly generating the required data.

• PN model can easily be connected to other applications as we have seen in the coordi-nation mechanism case or the FMECA method.

• Methods like FMECA identify the risks in a system as well as their cause and effectsrelationships. The most critical failure modes are found using the Risk Priority NumberRPN . As we have seen, the calculation of RPN relies a lot on expert opinion and maysometimes lack precision. If real data is available, PN modeling can provide a moreprecise measure of criticality via performance analysis. FMECA is also valuable toimagine mitigation action scenarios because the causes of failures are identified. How-ever, FMECA is not convenient to evaluate the impact of different mitigation actions onthe system performance. The performance evaluation side can be left to the PN model.We believe that the coupling FMECA with a PN model is quite original because thesetwo methods are complementary to each other in handling the risk management processgiven in section 5.1.

• Being able to evaluate different mitigation actions is a very attractive point for indus-trial applications. Often the resources are limited. Therefore, the decision makers areinterested in finding the best way of allocating these resources.


• Last but not the least, we recall that the applicability of the method is shown by areal industrial case study. We believe that the object oriented version is even morepromising for applications in large scale supply chains.

One can argue that the analysis via a commercial simulation software can also be usedinstead of PN. We believe that PN models for simulation are like what metaheuristics are fornear optimal solution procedures. Our model is more adaptable for a variety of industrialsituation than a model which is built using a commercial simulation software. Therefore, itis reusable more easily than a commercial simulation tool.

We believe that PN modeling for risk management will be followed by other researchersas well. As we have mentioned before, a recent survey is published in august 2010 on thePN-based application in supply chain networks [ZLW10]. Aside from providing various clas-sifications of different PN types according to their applications, the authors also present aseparate section where emerging applications are discussed. Here, risk management is men-tioned as an emerging application area and ([3]) is referenced in this domain.

Bibliography

[1] Gulgun Alpan, Van-Dat Cung, Fabien Mangione, and Gonca Tuncel. Collaboration, Align-ment and Coordination for Supply Chain Performance, chapter 7, Coordination mecha-nism as a mitigation action to manage Supply Chain Risks. Wiley, July 2010.

[2] Gonca Tuncel and Gulgun Alpan. A high-level petri net based modeling approach for riskmanagement in supply chain networks. In Proceedings of the 21st European Simulationand Modeling Conference - ESM 2007, pages CD–ROM, St. Julians, Malta, Nov. 2007.

[3] Gonca Tuncel and Gulgun Alpan. Risk assessment and management for supply chainnetworks: a case study. Computers in Industry, 61(3):250–259, 2010.

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Chapter 6

Conclusion and Perspectives

In this final chapter I will present some perspectives in relation to the research work presentedin this document. Some of these perspectives are immediate ones and the work is alreadystarted.

6.1 Perspectives in automobile industry

As we have seen in chapter 2, the starting point of our research in automobile industry wasthe assembly line flexibility but our major scientific contribution turned out to be in the carsequencing literature. This domain has already received a lot attention from researchers andthe advances in the domain are numerous. Therefore, unless we find a new play ground whichis similar to automobile assembly line context, I do not believe that we will invest more inthis domain.

Nevertheless, some of the work presented here can be improved. For instance, our defini-tion of spacing constraints can be refined on several points: For the calculation of N/P , themultiple temporization workstations are approximated by a two temporization workstation.We used a best, a worst or an average case scenarios for this approximation. This approach isoperationally viable and also commonly considered as a hypothesis in earlier works. However,we have already shown in our articles that this assumption results in a certain loss of precisionin the results. For instance, for a group of vehicles with Ti,j >> Tcycle a workstation may bemuch more under-constrained than for another group having Ti,j > Tcycle even though IFi,k

may be identical for both cases. Currently, such a situation is handled using the worst casescenario or accepting spacing constraints only with very high global IF . Our method canfurther be refined by calculating statistically significant Tsup and Tinf values to guarantee thecoverage of the temporizations with a certain confidence interval.

From an operations management point of view, I believe that the major issues in thisindustry is moving from the management of assembly line tasks towards supply of the lineand more globally the management of its supply chain. This supply chain is quite complexwith many partners and is widely stretched across the world. The scientists have come along way in providing high performance tools for production scheduling. The supply chaindimension needs to be integrated and we believe that research opportunities are abundant inthis domain. We are currently in contact with a major car manufacturer having operationalchallenges in its supply chain. If this contact reveals fruitful, we will start working on capacityrationing problems under lead time fluctuations due to distant outsourcing strategies used in

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90 CHAPTER 6. CONCLUSION AND PERSPECTIVES

the supply chain.

6.2 Perspectives in cross docking

As we have seen in section 3.4, the studies are very new in the domain of cross docking andhence the perspectives are numerous. Here, I will cite some of them:

6.2.1 Relaxing some assumptions

Even though some of the assumptions we have used in cross dock problems are deduced froman industrial case. However, the methods are not tested under a real industrial environment.The application may require the relaxation of some of these assumptions.

Loading trucks to their full capacity

One of the assumptions which is restrictive is that the trucks are loaded to their full capacity.One of the objectives of the cross docking is to use the resources as efficiently as possible.Full truck loads are justified in that sense. However, cross docking also seeks to reduce thedelivery lead times. Therefore, less-than-truck loads are also observed in a real cross dockenvironment. In a cross dock, a trade-off between resource and time utilization are to befound. As industries are becoming more sensitive in environmental issues, it is interesting toinvestigate this trade-off by relaxing this constraint. This relaxation can be done easily sincethe truck capacity is an input parameter. Instead of a fixed value, it can be defined as a rangeof values.

Managing the inbound process

In the case of multi-dock problems, we have considered a fixed input sequence which meansthat the operations manager in the platform does not have any control over the inboundprocess. This assumption is realistic in the case of production cross docks. That is, theinbound process is actually a production process and is controlled by a predetermined schedulebased on the daily production plan. In a distribution cross dock, where the inbound flowis generated by the arrival of trucks, this assumption does not hold. Furthermore, if theoperations manager can have a control on the inbound sequence, he may regulate betterthe outbound operations as well. This year, we have proposed a master’s project with theobjective of controlling the inbound sequence. The objective is to propose a method toreschedule the arrival sequence in a multi-dock environment and to observe the potential gainin operational costs, as well as the computational efficiency of our algorithms.

In the PhD thesis of Rim Larbi, we have already tested the effects of controlling theinbound process for the case of single receiving and shipping door cross docks. To this end,we have used the taboo search to reorganize the arrival sequence. The numerical results haveproven that the operational costs can be decreased by 30 to 45 % on the average. We believethat the operational costs will have a similar behavior in the case of multi-dock problems.What is especially interesting for us is to observe the computational gain under a controlledinbound process. Our initial hypothesis is that if the arrival sequence is controlled such thatthe products to a given destination can be regrouped, the number of states generated in ourmodels decreases.

6.2. PERSPECTIVES IN CROSS DOCKING 91

Finite resource capacities

Another assumption is that the man power is considered unlimited and we can perform asmany operations as we want at a given time. In reality these resources are scarce and eachoperator and/or equipment has his own specificities. For example, an operator may be spe-cialized in unloading but not in repackaging. Regular transpallets can be used for transportingbulky products while special equipment is necessary for fragile packages. Furthermore theoperators may have availabilities either predetermined by personal preferences or imposedby legislation. As a result, scheduling of operations are restricted by resource availabilities.Employee time-tables shall normally be an input data of the operations scheduling problems,which is not currently considered in our studies. As we have seen in chapter 3 the DP modelfor the multi-dock environment suffers from combinatorial complexity. Our solution was toimpose bounds on state generations. If we have time tables for employees and equipments,this information may be used in generating bounds.

Establishing time tables for employees is, in itself, a difficult but very interesting problem.Since September, I am co-advising with Bernard Penz a master’s project at NYK Logistics, atSaint Quentin Fallavier. NYK is a Japanese group which provides global logistics solutions.Their logistics platform at Saint Quentin Fallavier is experiencing resource allocation problemssince they have a fluctuating daily workload. In this master’s thesis, the objective is to providethe company with a tool which is capable of generating weekly and daily employee time tablesand equipment utilization schemes. These time tables should satisfy all operator and flowconstraints and optimize the use of the available human and machine resources. We arecurrently conducting field studies in the platform to make a list of all constraints relevant toour problem.

We believe that this master’s thesis will not only provide us some insights on how theresource utilization can be considered in previous models, but we also hope to have a sus-tainable relationship with the company to test our cross dock models. Cross docking is atechnique employed for certain flows in the platform. Our field study will hopefully provideus other clues with respect to the assumptions considered in our cross docking models.

6.2.2 Improving heuristic models

As we have seen in chapter 3, the hybrid heuristics perform well. Nevertheless they canfurther be improved by adding additional rules to cut-off further nodes from the DP model’ssolution space.

Heuristic rules we have presented here are based on the internal parameters of the problem,such as the input sequence for H1 and H2, or the storage level for H3. We can act on thedynamics of the nodes generated. For instance, it does not have a lot of practical sense tochange all outbound trucks at the same time. Hence, the number of changes that we canmake in between stages t and t + 1 can be limited, avoiding many nodes to be generated att+ 1.

In the previous section, we have discussed how some of the constraints can be relaxed.Some of these relaxations, such as controlling the inbound process may have a positive effecton the computational complexity of the heuristics as well. Hence, the heuristics can be coupledwith the methods which controls the inbound process.

Up to now, in the numerical tests, we have used rather small instance sets. The objectivewas to evaluate the performance of the heuristics compared to the optimal solution. Especially


hybrid heuristics can be tested on larger instances. In this case, no optimal solution isavailable. However, their performance can be evaluated compared to some other heuristics,such as, some greedy ones which simulate the behavior of a cross dock facility supervisor.

6.2.3 Some long term perspectives

With the master’s thesis of Onur Ozturk, co-supervised with M.-L. Espinouse, we have startedsome research projects on dock assignment problems with an original objective function whichis the minimization of the overlapping of material flows. As mentioned before this problem issimilar to the minimum crossing number in a graph and is shown to be NP-hard. The mixedinteger linear program proposed for the resolution can solve small instances. We have proposeda heuristic based on a 2-Opt method as a starting point but more elaborate heuristics need tobe tested. Especially, the literature on circuit board design in micro-electronics seems to beinteresting to investigate since there exist some similarities with our problem: in electroniccircuit board design the components shall be positioned on the board such that the overlappingof connections between the components are minimized. Our preliminary literature search givessome heuristic methods developed in this domain. Some of the ideas found in these heuristicscan be tested for dock assignment problems.

Another challenge is that, in the case of the cross docking, each problem corresponds toa snap-shot of an assignment at a given time period. When the set of trucks at the dockschanges, a new (possibly partial) assignment has to be found. For a model to be operational,this time dimension has to be considered. Either the method is fast enough and the model canbe used frequently to regenerate an assignment at each input data. Or a long term assignmentprocess must be considered but probably result in a complex model. Such a model can onlybe used in an off-line manner.

Finally, up to now, we have employed only deterministic methods in our models. Webelieve that use of stochastic models such as queueing theory could be interesting, especiallyfor dock assignment processes in the case of minimization of congestion inside the crossdock. To limit the congestion inside a cross dock, the inbound materials flow rate should bebalanced with respect to outbound traffic rate. In queueing models, we can easily observethat increasing the amount of incoming traffic above a certain threshold results in a completeblocking of the system. Such models can be used to observe the threshold in the case of crossdock problems.

6.3 Perspectives in ramp-up

Ramp-up management is a relatively new topic. Our literature and field studies have high-lighted several problems in LVHV industries. We have already addressed some of them butsome others are still open. As a first step, we would like to test the validity of our auditingtools on other industrial sectors. A master’s project will start in February 2011 at Volvo Aerounder my supervision. The master’s student, Francois Haller, has the mission to study thenew product hand-over from R&D Department to the production department (i.e. develop-ment to ramp-up phase) at Volvo Aero and some other companies in Sweden. This mastersstudy will provide us with a benchmark in the domain. Furthermore, we will also be able totest the auditing tools in another industrial context.

As we have seen in chapter 4, the models to analyze and control the steady state system’sbehavior (i.e. the mature production context) do not function well for the transitory state

6.3. PERSPECTIVES IN RAMP-UP 93

behavior of the production ramp-up. Almost all well known production planning modelsare steady state models. In the current industrial context, new product introduction takesan important part in the planning processes. For instance, if you discuss with operationsmanagers in companies who supply parts to the automobile industry, they will tell you thatabout 1/3 of their references are renewed every year. That is to say, they are producing inramp-up mode almost all the time. Hence, production models for transitory behavior mighteven be a reference on a daily basis. I believe that the models to control transitory productionphases will gain ground in the upcoming years. For the researchers in operations managementand industrial engineering, designing such models will be a nice perspective.

Some other interesting research directions in the domain are as follows:

Ramp-up is accompanied with a phase-out

As the title indicates, the ramp-up of a product are often accompanied by the phase-out ofan old product. During a certain period, these two products co-exist in the same productionenvironment and the production capacity is shared. This results in interesting capacity allo-cation problems. Furthermore, the phase-out period needs a particular attention as well sinceunless handled with care the phase-out may generate unused components and finished goodsstock due to obsolescence.

Supply chain design for new products

Supply chain design for new products is an interesting issue to investigate. As mentioned inchapter 4, in the LVHV industries, the supply chain design is not easy because on one handthe parts are very specific requiring high quality, on the other hand the demand is low hencecompanies de not have power over their suppliers. Very few studies consider the implicationof suppliers during ramp-up even though the suppliers play a major role in the fabrication of anew product. Managers should be aware of the fact that the ramp-up phase implies numerousparticipants and stake holders, not only inside the company but also among the supply chainpartners. When a ramp-up is engaged in a company for a new product, successive ramp-upsare also observed in the suppliers. The effects are very similar to the well known bull whipeffect observed in inventory management (i.e. the problems are propagated and amplifiedthough the supply chain since all upstream actors may experience a ramp-up phase at thesame time as the original equipment manufacturer). We note that up to now we only focus onliterature directly concerning ramp-up. This topic may be indirectly addressed in literatureabout supplier integration.

The trade-off between KPI

With the master’s thesis of Mostafa Tahmasebi, we have initiated some simulation basedmethods to investigate the trade-off between different performance indicators. This studygave some preliminary results but the number of numerical tests are not enough to draw solidconclusions. This study shall be pursued.

In our literature review and field study on ramp-up we have not observed any KPI relatedto customer satisfaction. However, this point is especially important for low volume industriesfor which the customers are uniquely identified with specific design requirements. Hence KPIrelated to customer satisfaction are of interest for such sectors. For instance, percentage ofcustomers (or products) delivered on time may be an important time related KPI which is


not addressed. Similarly, the quality measures are mainly company-internal measures andquality perceived by customers are not addressed at all. Follow up of such KPI might beinteresting to implement in LVHV context.

Risk management of the ramp-up phase

Meier and Homuth mention a survey realized by the Kuhne Institute of the St Gallen Uni-versity conducted at automobile suppliers [MH06]. According to this survey, only 43% ofthe ramp-ups are economically and technically successful. 24% have not achieved the desiredgoal, neither technically nor economically, another 24% are successful economically, but arenot satisfying technically and a further 9% have reached the technical goal, but miss theestimated costs. All in all, 43% of the ramp-ups were considered successful.

This survey highlights very well the risk bearing nature of ramp-up period. One per-spective could be to investigate to what extent our PN based risk management tool can beadapted to study risks during ramp-up period.

6.4 Perspectives in supply chain risk management

As we have seen in the case study, in a real industrial environment the sources of uncertaintiesare numerous and in order to get reliable results we need to have reliable estimation ofthese uncertainties. For instance, a variation in inter arrival times of the customer demand,operation times, supplier selection decisions, shipment size, production mix, inventory levels,and production and transportation failure rates can lead to different results and have differenteffects. The analysis may even change for different branches of a same company. The riskmanagement model shall be modular enough to permit the system designers to carry outthe evaluation by themselves under their own operating conditions. We believe that ourmodel with CPN objects are modular and flexible however their current structure do notgive a plug-and-play functionality. Therefore, if some changes need to be made on basicmodules, the user may have some difficulties to implement them unless he already knows howto manipulate PN models. We have recently recruted a new colleague, Pierre David, at G-SCOP laboratories. His research concerns using meta-models (e.g. SysML) to translate semi-automatically the specifications of a product or process into an analyzable model such as Petrinets. A collaboration idea is to use the techniques developed by P. David to validate alreadyexisting CPN objects of our model and to generate automatically (or semi-automatically) newones. We have started writing a proposal for the next PHC call for projects for the Bosphorusprogram, a collaboration program with Turkey.

In the actual industrial context, the dependency between the entities in a supply networkis increased considerably. Just like coordination among supply chain partners in early 2000,we believe that the concept of risk sharing among supply chain partners is now becomingan important issue in supply chain risk management. A perspective in this domain is toinvestigate if the notion of risk sharing can be implemented in our model.

To wrap up, I would like to say who knows what other perspectives the cave of FortyThieves reserves for us?

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