advanced planning and scheduling in manufacturing and supply …978-3-319-27523... ·...

Advanced Planning and Scheduling inManufacturing and Supply Chains

ThiS is a FM Blank Page

Yuri Mauergauz

Advanced Planning andScheduling inManufacturing and SupplyChains

Yuri MauergauzSophus GroupMoscowRussia

ISBN 978-3-319-27521-5 ISBN 978-3-319-27523-9 (eBook)DOI 10.1007/978-3-319-27523-9

Library of Congress Control Number: 2016933485

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Preface to the English Edition

Despite the relatively large number of books related to production planning

published in English, up to now information constituting the subject of Advanced

Planning and Scheduling has not been gathered together. This situation inspired the

author to present an English translation of his Russian-language book.

This book was conceived as a guide to modern methods of production planning,

based on fairly new scientific achievements and various rules of thumb of practical

planning. Most of the calculation methods are illustrated with numerical examples.

Attached to the English edition is a set of programs for calculating production

schedules and an example of an ERP system operating in the cloud.

The author expresses his profound gratitude to Federica Corradi Dell’Acqua of

Springer publishers. Her systematic support allowed this project to be implemented.

Moscow, Russia Yuri Mauergauz

v

Preface to the Russian Edition

At the end of the last century, a large new field of knowledge developed. Nowadays,

it is called “industrial engineering” and is a creative application of the methods and

principles of various scientific disciplines to achieve and maintain a high level of

productivity and profitability in modern industrial enterprises.

The application of industrial engineering is inextricably linked with the use

of quantitative methods using information that circulates in the production system,

and such methods often have complex mathematical justification. Historically,

the concept of industrial engineering started to be used after wide application

of methods known as “operations research”. Another name for these methods is

“management science”, now more commonly called “industrial engineering”.

Since the foundation of industrial engineering is quite sophisticated mathemati-

cal techniques, its application possibilities are determined largely by the available

computing power. Originally, computers were created to solve complex scientific

problems. Subsequently, this equipment started to be used to develop automated

control systems including production management systems.

The introduction of personal computers changed dramatically the possibilities

and the main focus of application of computer technology. The main objective

of computerization in the late twentieth century was automation of accounting

of a variety of resources and operations with them, i.e. information storage. The

automated control systems of enterprises were mainly designed to collect and

integrate data referring to production and sales. Therefore, the development of

industrial engineering at that time was mostly of a scientific and theoretical nature.

In the early twenty-first century, however, the situation changed dramatically.

First of all, against the background of rising resource prices, the issue of production

efficiency is becoming more and more important. In addition, it was found that,

despite their great diversity, the number of accounting problems is limited and most

problems had already been solved, while the increasing capabilities of computer

technology allow more complex problems to be solved. As a result, researchers

and production managers began to turn to the problems of enhancing production

management.

There was a sharp increase in the number of articles in the field of industrial

engineering and a rapid increase in the number of relevant scientific journals.

Today, worldwide, there are at least 30 international English-language journals in

vii

which thousands of scientific articles on industrial engineering are published

annually. In addition, there are a number of national engineering journals, and

in some countries, such as Spain and Iran, they are published with simultaneous

translation into English.

The field of industrial engineering includes management aspects such as the

location of enterprises, determining the range of products, selection of necessary

processes, organization of production divisions, etc. Many of these management

objectives refer to pre-production, but not its realization. The effective implemen-

tation of production is only possible with organized and comprehensive sound

planning, which is actually the final component of industrial engineering.

Until the end of the last century, production planning was mainly based on the

knowledge and experience of the planners themselves who used quite elementary

methods of calculation for different purposes. The use of computer technology, for

the most part, was limited to calculation of the number of products and resources

required.

Due to the complexity of the mathematical description of plans, their optimiza-

tion appeared to be possible after the introduction of powerful personal computers

at the beginning of this century. The relevant methods were used to create a number

of new production control systems, known as APS and MES. In general, the new

planning methods based on complex mathematical models are called Advanced

Planning and Scheduling (AP&S). This book is intended for readers whose

activities are related to production planning, though in different business areas.

First of all, the book is intended as a reference guide for operating production

managers. As the workload of these specialists does not allow them to engage in a

consistent and detailed study of the various methods, the book is designed so that

almost every section, and sometimes even an individual paragraph, can be read

independently of the other sections. At the same time, wherever possible when

describing a method, reference is made to the preceding discussion of the method,

to allow deeper examination of the material.

To make the presentation of each section independent of the previous text,

most of the methods and examples use the same designations of variables and

parameters, and these designations are listed in Appendix A. In those cases where

the designation does not coincide or is not referred to in Appendix A, it is defined in

the text. Each example is accompanied by the method reduced to a final calculation.

The author hopes that this structure will be convenient for developers of production

planning software as well as for production managers.

Not all planning methods described in this book are useful in practice. This

applies to a number of problems and their solutions, which provide a scientific basis

for comparison and a reference sample for other methods, which in turn may be

used in practice.

On the other hand, the book is constructed to provide the opportunity to study

the material consistently. The book is divided into two parts, the first of which

is dedicated to detailed description of models of planning, and the second part

describes the processes carried out on the basis of these models. Some of these

viii Preface to the Russian Edition

models are quite complex, and at first acquaintance their study can be skipped. This

construction is to facilitate learning by researchers, postgraduates, and students.

The challenge in writing this book was the selection of materials and the

sequence of their presentation. An enormous number of different methods of

production planning have been developed. In particular, G. Halevi’s reference

book on production planning methods dated 2001 describes 110 methods, which,

of course, vary to a large extent in the degree of distribution and application. This

book includes those models and planning processes which by the time of writing

were in focus in the scientific literature. It was assumed that production planning

itself is closely connected to the planning of inventory because the result of the

manufacturing process is stock buildup.

The contents of the book, for the most part, are based on the results of scientific

papers contained in a number of English-language guides, monographs, and articles

written at the end of the twentieth and beginning of the twenty-first century. The

author has also tried whenever possible to use the available, albeit few, modern

Russian-language works. Materials relating to the period of development of com-

puter systems in the Soviet Union in the 1970s and 1980s have also been used. The

structure and nature of any presentation always depends largely on the author’s

position. In this case, when considering methods of production planning, special

attention is paid to its regularity and dynamics, i.e. a periodic recurrence and at the

same time the need to introduce various changes, including urgent ones.

Different scientific disciplines are used in themethods of production planning. Each

discipline has its own set of traditional symbols. In this book, itwas important to ensure

consistent use of symbols, so one designation system was chosen as basic. Therefore,

the nomenclature of symbols accepted in scheduling theory is used throughout; in

other cases, some symbols may be different from the conventional ones.

The author is grateful to Professor A.L. Ryzhkowhose comments and suggestions

helped to improve the presentation significantly.


Preface to the Russian Edition ix

Annotation

Advanced Planning and Scheduling (AP&S) in Productionand Supply Chains

The book consists of two parts, the first of which considers construction of refer-

ence and mathematical planning models, production bottleneck models, and multi-

criteria models; examples of such models are provided. The methods of forecasting

and aggregate demand are discussed; background information about the storage and

data processing methods for planning are provided.

The second part analyses various models of stocks planning and the rules for

calculating safety stocks; it also describes the stocks dynamics in the supply chain.

Various methods of batch sizing are detailed. Production planning is studied at

several levels: planning of shipment to customers, calendar scheduling, and opera-

tional planning. Operational planning is considered separately for one-stage and

multi-stage problems as well as for different multi-criteria problems. For some

problems of multi-criteria, scheduling by the methods described in the book special

software is developed.

The book can be used as a reference for modern planning methods as well as a

teaching aid. It is intended for employees of planning and production services,

specialists in enterprise information management systems, and researchers and

graduate students involved in production planning. The book can be used by students

at technical colleges as a guide when writing course papers and graduate theses.

A description of a collection of production schedule programs and an example of

the ERP system operating in the cloud is included in the book.


xi

Contents

Part I Modeling

1 Reference Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1 Modelling of Business Process . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Concept of Reference Model . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.2.1 Reference Models in Supply Chains . . . . . . . . . . . . . . 6

1.2.2 Reference Modelling Methodology . . . . . . . . . . . . . . . 7

1.3 Production Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3.1 Basic Types of Production . . . . . . . . . . . . . . . . . . . . . 9

1.3.2 Production Scale and Strategy . . . . . . . . . . . . . . . . . . 13

1.4 Advanced Planning in IT Systems . . . . . . . . . . . . . . . . . . . . . . 15

1.4.1 Planning in IT Systems . . . . . . . . . . . . . . . . . . . . . . . 15

1.4.2 Popularity and Effects of Advanced Planning . . . . . . . 19

1.5 IT System Interaction Standards . . . . . . . . . . . . . . . . . . . . . . . 21

1.6 Quality Parameters in Supply Chains . . . . . . . . . . . . . . . . . . . . 24

1.6.1 Markets and Their Main Properties . . . . . . . . . . . . . . . 25

1.6.2 Quality Parameters and Different Supply Chain

Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.6.3 Balanced Scorecard . . . . . . . . . . . . . . . . . . . . . . . . . . 28

1.7 Utility of Quality Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 31

1.7.1 Concept of Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

1.7.2 Typical Utility Functions . . . . . . . . . . . . . . . . . . . . . . 34

1.7.3 Utility Functions in Business Process Quality

Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2 Mathematical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.1 Simplest Planning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.1.1 Classical Supply Management Model . . . . . . . . . . . . . 43

2.1.2 Continuous Linear Optimization Model . . . . . . . . . . . . 45

2.2 Correlations Between Mathematical and Reference Models . . . . 52

2.2.1 Main Criteria and Constraints . . . . . . . . . . . . . . . . . . . 52

2.2.2 Standard Classification of Planning Optimization

Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

xiii

2.2.3 Production Scale and Plan Hierarchy in Classification . . . 55

2.3 Priority Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

2.3.1 Simple Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

2.3.2 Some Useful Theorems . . . . . . . . . . . . . . . . . . . . . . . 62

2.3.3 Combined Priority Rules . . . . . . . . . . . . . . . . . . . . . . 62

2.4 Production Intensity and Utility of Orders . . . . . . . . . . . . . . . . 64

2.4.1 Production Intensity . . . . . . . . . . . . . . . . . . . . . . . . . . 65

2.4.2 Dynamic Utility Function of Orders . . . . . . . . . . . . . . 70

2.5 More Complex Models of Linear Optimization . . . . . . . . . . . . 74

2.5.1 Integer Linear Optimization Model . . . . . . . . . . . . . . . 74

2.5.2 Integer Linear Optimization Models with Binary

Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

2.6 Fixed Job Sequence Models . . . . . . . . . . . . . . . . . . . . . . . . . . 78

2.6.1 Branch-and-Bound Method with Minimum Cumulative

Tardiness Tw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

2.6.2 Branch-and-Bound Method with Maximum Average

Utility V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

3 Production Bottlenecks Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

3.1 Theory of Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

3.1.1 Fundamentals of Theory of Constraints . . . . . . . . . . . . 89

3.1.2 Bottleneck Operation Planning . . . . . . . . . . . . . . . . . . 92

3.1.3 Planning for Buffers, Ropes, and Non-bottleneck

Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

3.1.4 Simple Example of Theory of Constraints

in Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

3.1.5 Theory of Constraints in Process Manufacturing . . . . . 98

3.1.6 Review of TOC Applications . . . . . . . . . . . . . . . . . . . 100

3.2 Theory of Logistic Operating Curves . . . . . . . . . . . . . . . . . . . . 101

3.2.1 Production (Logistics) Variables . . . . . . . . . . . . . . . . . 101

3.2.2 Some Notions Used in Queuing Theory . . . . . . . . . . . . 105

3.2.3 Plotting Logistic Operating Curves . . . . . . . . . . . . . . . 107

3.2.4 Main Properties of Logistic Curves . . . . . . . . . . . . . . . 110

3.3 Application of Logistic Operating Curves . . . . . . . . . . . . . . . . 110

3.3.1 Logistic Positioning . . . . . . . . . . . . . . . . . . . . . . . . . . 110

3.3.2 Bottleneck Analysis and Improvements . . . . . . . . . . . . 111

3.3.3 Evaluation of Overall Production Performance . . . . . . 112

3.4 Optimal Lot Sizing for Production Bottlenecks . . . . . . . . . . . . . 116

3.4.1 Lot Sizing Heuristic . . . . . . . . . . . . . . . . . . . . . . . . . . 116

3.4.2 Analysis of Heuristic Solutions . . . . . . . . . . . . . . . . . . 119

3.5 Hierarchical Approach to Machinery Load Management . . . . . . 122

3.5.1 Principles of Workload Control Concept . . . . . . . . . . . 123

3.5.2 Example of Application of Controlled Load Approach . . . 124

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

xiv Contents

4 Multi-criteria Models and Decision-Making . . . . . . . . . . . . . . . . . . 127

4.1 Basic Concepts in Multi-criteria Optimization Theory . . . . . . . 127

4.1.1 Definition of Multi-criteria Optimization Problems . . . 127

4.1.2 Pareto Optimality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

4.1.3 Main Methods of Solving Multi-criteria Planning

Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

4.1.4 Analytical Method of Constructing a Trade-Off Curve . . . 136

4.2 Optimized Multi-criteria Lot Sizing . . . . . . . . . . . . . . . . . . . . . 138

4.2.1 Lot Sizing Based on Costs and Equipment . . . . . . . . . 138

4.2.2 Analytical Lot Sizing with Two Criteria: Setup Time

and Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

4.3 Example of Multi-scheduling Problem . . . . . . . . . . . . . . . . . . . 143

4.3.1 Special ε-Neighbourhood of Efficiency Points . . . . . . . 144

4.3.2 Solving Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

4.4 Methods of Decision-Making Theory in Planning Problems . . . 150

4.4.1 Some Information from the Decision Making Theory . . . 150

4.4.2 Example of the Planning Problem Requiring Decision

Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

4.4.3 Decision-Making Based on the Guaranteed Result

Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

4.4.4 Optimistic Decision-Making . . . . . . . . . . . . . . . . . . . . 157

4.5 Applications of Complex Decision-Making Methods . . . . . . . . 158

4.5.1 Hurwitz Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

4.5.2 Savage Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

4.5.3 Shifted Ideal Method . . . . . . . . . . . . . . . . . . . . . . . . . 160

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

5 Data for Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

5.1 Composition of the Data Used for Planning . . . . . . . . . . . . . . . 163

5.1.1 Archives of Design-Engineering Documentation and

Orders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

5.1.2 Reference Data and Standards . . . . . . . . . . . . . . . . . . 167

5.1.3 Databases of Transactional IT Systems . . . . . . . . . . . . 169

5.1.4 Decision Support Databases . . . . . . . . . . . . . . . . . . . . 170

5.1.5 Knowledge Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

5.2 Data Storage and Management . . . . . . . . . . . . . . . . . . . . . . . . 174

5.2.1 Relational Databases . . . . . . . . . . . . . . . . . . . . . . . . . 174

5.2.2 Concept of Object-Oriented Databases . . . . . . . . . . . . 176

5.2.3 Database Management Systems . . . . . . . . . . . . . . . . . 177

5.2.4 Tiered Data Storage . . . . . . . . . . . . . . . . . . . . . . . . . . 178

5.2.5 Distributed Databases . . . . . . . . . . . . . . . . . . . . . . . . . 179

5.2.6 Service Oriented Architecture of IT Systems . . . . . . . . 181

5.2.7 On-Line Analytical Processing . . . . . . . . . . . . . . . . . . 183

5.3 Information Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186

Contents xv

5.3.1 Internal Data Communication . . . . . . . . . . . . . . . . . . . 186

5.3.2 Data Transfer Between Enterprises . . . . . . . . . . . . . . . 187

5.3.3 Information Exchange in Different Types of

Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

5.3.4 Information Exchange Automation . . . . . . . . . . . . . . . 191

5.3.5 Use of Cloud Environment . . . . . . . . . . . . . . . . . . . . . 194

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

6 Demand Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

6.1 Demand Modelling Based on Time Series Analysis . . . . . . . . . 199

6.2 Main Methods of Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . 201

6.2.1 Moving Average Method . . . . . . . . . . . . . . . . . . . . . . 201

6.2.2 Exponentially Smoothing Forecasting . . . . . . . . . . . . . 203

6.2.3 Trend Adjusted Exponential Smoothing . . . . . . . . . . . 204

6.2.4 Trend and Seasonality Adjusted Exponential

Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206

6.3 Demand Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208

6.4 Aggregated Demand Forecasting . . . . . . . . . . . . . . . . . . . . . . . 210

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214

7 Examples of Advanced Planning Models . . . . . . . . . . . . . . . . . . . . . 215

7.1 Joint Operation Model of APS System and ERP System

from SAP R/3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

7.1.1 Main Business Process Attributes in Various

Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216

7.1.2 Software Modules for Planning Solutions . . . . . . . . . . 218

7.1.3 Planning Modules Interaction . . . . . . . . . . . . . . . . . . . 219

7.2 Reference Model of Production Planning for Instrument

Engineering Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221

7.2.1 Initial Planning Status Analysis . . . . . . . . . . . . . . . . . 221

7.2.2 Decision Support Database . . . . . . . . . . . . . . . . . . . . . 223

7.3 Mathematical Model in Chemical Industry . . . . . . . . . . . . . . . . 226

7.3.1 Analytical Structure of Model . . . . . . . . . . . . . . . . . . . 226

7.3.2 Objective Function and Constraints . . . . . . . . . . . . . . . 229

7.3.3 Some Results of Modelling . . . . . . . . . . . . . . . . . . . . . 233

7.4 Rapid Supply Chain Reference Model in Clothing Industry . . . . 234

7.5 Schedule Model for a Machine Shop . . . . . . . . . . . . . . . . . . . . 237

7.5.1 Schedule Model with Specified Processing Stages . . . . 238

7.5.2 Optimality Criteria and Constraints . . . . . . . . . . . . . . . 239

7.6 Multi-stage Logistics Chain Model . . . . . . . . . . . . . . . . . . . . . 241

7.6.1 Some Notions in Logistics Chain Modelling . . . . . . . . 241

7.6.2 Dynamic Logistics Chain Optimization Model

in Multi-stage Production . . . . . . . . . . . . . . . . . . . . . . 241

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

xvi Contents

Part II Planning Processes

8 Single-Echelon Inventory Planning . . . . . . . . . . . . . . . . . . . . . . . . . 247

8.1 Inventory Types and Parameters . . . . . . . . . . . . . . . . . . . . . . . 247

8.2 Inventory Management Models . . . . . . . . . . . . . . . . . . . . . . . . 248

8.2.1 Model with Fixed Quantity of Order . . . . . . . . . . . . . . 249

8.2.2 Model with Fixed Reorder Cycle . . . . . . . . . . . . . . . . 250

8.2.3 Two-Tier Inventory Management Model . . . . . . . . . . . 251

8.2.4 Benchmarking of Inventory Management Models . . . . 253

8.2.5 Kanban Inventory Management Model . . . . . . . . . . . . 254

8.3 Inventory Management Model Under Uncertainty . . . . . . . . . . 256

8.3.1 Customer Service Level . . . . . . . . . . . . . . . . . . . . . . . 256

8.3.2 Shortages Permitted Inventory Management Model . . . 257

8.3.3 Demand Distribution Functions . . . . . . . . . . . . . . . . . 258

8.3.4 Newsvendor Problem . . . . . . . . . . . . . . . . . . . . . . . . . 260

8.4 Inventory Management Using Logistic Operating Curves . . . . . 262

8.4.1 Storage Curves and Their Applications . . . . . . . . . . . . 262

8.4.2 Finished Product Inventory Sizing to Optimize the

Overall Production Performance . . . . . . . . . . . . . . . . . 264

8.5 Safety Stock Sizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265

8.5.1 Calculation of Safety Stock with Random Demand . . . 266

8.5.2 Sizing of Safety Stock with Two Random Variables . . . 267

8.5.3 Sizing of Safety Stock with Three Random Variables . . . 269

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270

9 Supply Chain Inventory Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . 273

9.1 Stock Distribution Planning in the Chain . . . . . . . . . . . . . . . . . 273

9.1.1 DRP Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273

9.1.2 Regular Maintenance of DRP Tables . . . . . . . . . . . . . . 276

9.1.3 Parallel Multi-product Planning . . . . . . . . . . . . . . . . . 278

9.1.4 Inventory Dynamics at Long Lead Cycles . . . . . . . . . . 279

9.2 Supply Chain Fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281

9.2.1 Bullwhip Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281

9.2.2 Bullwhip Effect Factors . . . . . . . . . . . . . . . . . . . . . . . 284

9.2.3 Methods of Reducing Supply Chain Fluctuations . . . . . 286

9.3 Application of Logistics Operating Curves in Supply Chains . . . 289

9.4 Inventory Echelon Accounting . . . . . . . . . . . . . . . . . . . . . . . . 292

9.4.1 Inventory Echeloning . . . . . . . . . . . . . . . . . . . . . . . . . 292

9.4.2 Sequential Supply Chain . . . . . . . . . . . . . . . . . . . . . . 293

9.4.3 Supply Chain with Distribution . . . . . . . . . . . . . . . . . . 297

9.4.4 Dependency Between Echelon Stock and Number

of Links of One Level in the Supply Chain . . . . . . . . . 299

9.5 Inventory Planning in Spare Parts Supply Chains . . . . . . . . . . . 300

9.5.1 METRIC Method in Spare Parts Supplies . . . . . . . . . . 301

Contents xvii

9.5.2 Inventory Planning for Central Spare Parts Storage Using

(R,Q) Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305

9.6 Coordinated Planning Between Two Supply Chain

Members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311

10 Planning of Supplies to Consumers . . . . . . . . . . . . . . . . . . . . . . . . . 313

10.1 Sales and Operation Planning . . . . . . . . . . . . . . . . . . . . . . . . . 313

10.1.1 Interrelation Between Various Planning Directions with

Sales and Operations Plan . . . . . . . . . . . . . . . . . . . . . 313

10.1.2 Sales and Operation Planning Methods . . . . . . . . . . . . 315

10.2 Sales and Operation Plan Optimization Using Linear

Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318

10.2.1 Single Aggregated Product Group Optimization . . . . . . 319

10.2.2 More Complex Case of Optimization of Sales and

Operations Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322

10.3 Customized Reservation of Products . . . . . . . . . . . . . . . . . . . . 326

10.3.1 Business Process of Response to New Orders . . . . . . . 326

10.3.2 Arrangement of Orders . . . . . . . . . . . . . . . . . . . . . . . . 327

10.3.3 Running ATP Process . . . . . . . . . . . . . . . . . . . . . . . . 329

10.4 Agreement of Order Specifications with Customers . . . . . . . . . 331

10.4.1 Problem Criteria and Their Evaluation . . . . . . . . . . . . 331

10.4.2 Selection of Ordered Product Analogues . . . . . . . . . . . 332

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338

11 Lot Sizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339

11.1 Classification of Lot-Sizing Problems . . . . . . . . . . . . . . . . . . . 339

11.1.1 Lot Properties and Main Problems . . . . . . . . . . . . . . . 339

11.1.2 Lot-Sizing Problems with No Capacity Limits . . . . . . . 341

11.1.3 Lot-Sizing Problems with Limited Capacities and Large

Planning Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342

11.1.4 Lot-Sizing Problems with Limited Capacities and Small

Planning Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343

11.2 Constant Demand Lot-Sizing Problems . . . . . . . . . . . . . . . . . . 344

11.2.1 Models with Gradual Inventory Replenishment . . . . . . 345

11.2.2 Model Applicable to the Machinery Industry If No Cost

Information Is Available . . . . . . . . . . . . . . . . . . . . . . . 347

11.2.3 Three-Parameter Models for Machinery Industry . . . . . 349

11.2.4 Lot Sizing at Discounted Prices . . . . . . . . . . . . . . . . . 351

11.3 Lot Sizing at Variable Demand and Limited Planning

Horizon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352

11.3.1 Exact Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353

11.3.2 Heuristic Silver–Meal Algorithm . . . . . . . . . . . . . . . . 356

11.3.3 Part Period Balancing . . . . . . . . . . . . . . . . . . . . . . . . . 359

11.3.4 Groff’s Heuristic Rule . . . . . . . . . . . . . . . . . . . . . . . . 360

xviii Contents

11.3.5 Period Order Quantity . . . . . . . . . . . . . . . . . . . . . . . . 362

11.4 Lot Sizing with Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . 363

11.5 Multi-product Deliveries and Orders . . . . . . . . . . . . . . . . . . . . 366

11.5.1 Optimal Multi-product Lot Sizing . . . . . . . . . . . . . . . . 366

11.5.2 Multi-product Deliveries over Multiple Periods . . . . . . 368

11.5.3 Power-of-Two Policies for Multi-product

Deliveries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371

12 Production Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373

12.1 Master Production Planning . . . . . . . . . . . . . . . . . . . . . . . . . . 373

12.1.1 Master Planning as Product Tables . . . . . . . . . . . . . . . 374

12.1.2 Group Master Planning . . . . . . . . . . . . . . . . . . . . . . . 379

12.1.3 Master Production Plan Optimization . . . . . . . . . . . . . 381

12.2 Material Requirement Planning . . . . . . . . . . . . . . . . . . . . . . . . 383

12.2.1 Production Lot Duration . . . . . . . . . . . . . . . . . . . . . . . 384

12.2.2 Optimal Production Lot Sizing . . . . . . . . . . . . . . . . . . 386

12.2.3 Analysis of the Material Requirement Plan . . . . . . . . . 390

12.3 Project-Based Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391

12.3.1 Critical Path Method . . . . . . . . . . . . . . . . . . . . . . . . . 391

12.3.2 Cost Optimization at Various Project Stages . . . . . . . . 394

12.4 Stability of Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398

12.4.1 Quantitative Evaluation of Planning Stability . . . . . . . 399

12.4.2 Methods of Planning Stability Improvement . . . . . . . . 401

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404

13 Shop Floor Scheduling: Single-Stage Problems . . . . . . . . . . . . . . . . 405

13.1 Single-Machine Scheduling with Minimized Overdue

Penalties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405

13.1.1 Schedule with the Minimum of Delayed Jobs . . . . . . . 406

13.1.2 Scheduling with Minimum Weighted Tardiness

per Each Job . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406

13.1.3 Schedule Optimization with Earliness/Tardiness . . . . . 409

13.2 Common Shipment Date Scheduling . . . . . . . . . . . . . . . . . . . . 410

13.2.1 Fixed Date Schedule Optimization . . . . . . . . . . . . . . . 410

13.2.2 More Complex Cases of Scheduling with Fixed Date . . . 412

13.2.3 Selection of Optimal Midpoint Date for Shipping . . . . 413

13.3 Some Other Scheduling Problems for Jobs with Fixed

Processing Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415

13.3.1 Schedules for the Case of Several Jobs, the Part of

Which Has the Preset Sequence . . . . . . . . . . . . . . . . . 415

13.3.2 Scheduling of Jobs with Different Arrival Time . . . . . . 417

13.3.3 Scheduling of Jobs with Different Arrival Time and

Different Shipment Time . . . . . . . . . . . . . . . . . . . . . . 418

13.3.4 Job Sequence-Based Setup Time Scheduling . . . . . . . . 419

Contents xix

13.4 Periodic Scheduling with Lots of Economic Sizes . . . . . . . . . . 421

13.4.1 Equal-Time Schedules for All Products . . . . . . . . . . . . 421

13.4.2 Variable-Time Schedules for Different Products . . . . . 423

13.5 Group Technology in Schedules for a Single Machine . . . . . . . 426

13.5.1 Group Scheduling for Series Batches . . . . . . . . . . . . . 427

13.5.2 Group Scheduling for Parallel Batches with Minimum

Tardiness Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . 430

13.5.3 Group Scheduling for Parallel Batches with Maximum

Average Utility Criterion . . . . . . . . . . . . . . . . . . . . . . 432

13.6 Parallel Machine Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . 440

13.6.1 Identical Parallel Machine Scheduling . . . . . . . . . . . . . 440

13.6.2 Schedules for Parallel Unrelated Machines . . . . . . . . . 442

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445

14 Shop Floor Scheduling: Multi-stage Problems . . . . . . . . . . . . . . . . 447

14.1 Synchronized Flowshop Production . . . . . . . . . . . . . . . . . . . . . 447

14.1.1 Discrete Product Lines . . . . . . . . . . . . . . . . . . . . . . . . 448

14.1.2 Lines for Process Production . . . . . . . . . . . . . . . . . . . 449

14.1.3 Flexible Flow Lines . . . . . . . . . . . . . . . . . . . . . . . . . . 450

14.2 Automated Assembly Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . 452

14.2.1 Scheduling for Unpaced Assembly Lines . . . . . . . . . . 453

14.2.2 Scheduling for Paced Assembly Line . . . . . . . . . . . . . 456

14.2.3 Scheduling for Mixed Assembly Lines . . . . . . . . . . . . 460

14.3 Unsynchronized Flowshop Production . . . . . . . . . . . . . . . . . . . 462

14.3.1 Modelling for Unsynchronized (Discontinuous)

Flow Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463

14.3.2 Optimization for Two-Machine Group Flow Lines . . . . 466

14.3.3 Campbell, Dudek, and Smith Algorithm . . . . . . . . . . . 468

14.3.4 Nawaz, Enscore, Ham Algorithm . . . . . . . . . . . . . . . . 469

14.4 Job-Shop Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470

14.4.1 Shifting Bottleneck Algorithm . . . . . . . . . . . . . . . . . . 471

14.4.2 Job-Shop Production Scheduling Using Dynamic List

Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483

15 Multi-criteria Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485

15.1 Just-in-Time Production Scheduling . . . . . . . . . . . . . . . . . . . . . 485

15.1.1 Starting Group of Jobs with Fixed Sequence . . . . . . . . 485

15.1.2 Scheduling for Identical Parallel Machines with

Common Shipment Date . . . . . . . . . . . . . . . . . . . . . . 489

15.2 Multi-objective Algorithms for Some Simple Production

Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 490

15.2.1 Scheduling for Two-Machine Flowshop Production . . . 490

15.2.2 Schedule for Parallel Uniform Machines . . . . . . . . . . . 493

15.2.3 Some Other Problems and Solving Challenges . . . . . . . 499

xx Contents

15.3 Scheduling Based on Cost and Average Orders Utility . . . . . . . 501

15.3.1 Sequenced Job Scheduling with Sequence-Dependent

Setups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502

15.3.2 Group Scheduling for Parallel Batches Based on

Maximum Average Utility and Minimum

Setup Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

15.4 Application of Decision Theory Methods . . . . . . . . . . . . . . . . . 515

15.4.1 Application of Savage Principle for

Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516

15.4.2 Application of Hurwitz Principle for

Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . 518

15.5 Decision-Support Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519

15.5.1 Decision-Support System for Hybrid Flow Lines . . . . . 519

15.5.2 Some Other Decision-Support Systems . . . . . . . . . . . . 521

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523

Appendix A: Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525

Appendix B: Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527

Appendix C: Classification Parameters of Schedules . . . . . . . . . . . . . . . 529

C.1 Parameters in Field α . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529

C.2 Parameters in Field β . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 530

C.3 Parameters in Field γ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 530

Appendix D: Production Intensity Integral Calculations . . . . . . . . . . . . 533

Appendix E: Scheduling Software Based on Order Utility Functions . . . 537

E.1 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537

E.2 Description of Work with File1.xls . . . . . . . . . . . . . . . . . . . . . . . . 537








Appendix F: Using Clobbi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561

F.1 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561

F.2 Description of Planning Possibilities in the System . . . . . . . . . . . . . 563

F.3 Description of Service Operation . . . . . . . . . . . . . . . . . . . . . . . . . . 564

F.4 Clobbi Service Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566

F.5 Online Registration of Manufacturing Events . . . . . . . . . . . . . . . . . 568

F.6 Clobbi Commercial Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569

Contents xxi

About the Author

Yuri Mauergauz is an Assistant Professor and a consultant of Sophus Group,

Moscow, Russia. He gained his PhD from the St. Petersburg Navy Institute in

1970. He has worked at machine-building plants and research institutes and also

taught at the Urals and Odessa technical universities. He has published around

80 research papers and 3 books dedicated to the application of computer engineering

in production planning.

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advanced planning and scheduling in manufacturing and supply …978-3-319-27523... ·...

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