decision support system prototype for supply network ...decision support system prototype for supply...

Decision Support System Prototype for Supply Network

Configuration Planning and Operations Scheduling in

the Machine Tool Industry: a case study

Julien Maheut*, Juan Manuel Besga and Jone Uribetxeberria

4/13/2012 1 *Corresponding author: Julien Maheut - [email protected] Julien Maheut – [email protected]

Outline

I. Introduction

II. Literature Review

III. The “Stroke” concept

IV. The Decision Support System Prototype

I. Brief introduction

II. The Data Base enable alternative operations

III. The Algorithm for complete enumeration

IV. The Simulation Tool

V. Some results

VI. Conclusions & further works

4/13/2012 Julien Maheut – [email protected] 2

Outline

I. Introduction








V. Some results



Introduction

This presentation aims to introduced:

A decision support system prototype

A real supply network configuration problem and the operations scheduling problem

A case study of a company that assembles highly customised machine tools in several

European plants

Three relevant aspects of the DSS will be introduced:

1. A novel database structure is described able to consider :

Alternative operations (purchasing, production, routing and transport)

Alternative BOMs (upgrading, reconfiguring custom products)

2. An algorithm for complete enumeration to determine all the feasible solutions, and also

each solution cost and delivery time, is preliminary assessed.

3. A multi-agent-based simulator:

Evaluates the different KPIs handled by the company for each alternative solution (e.g., workload

plants, plants cost, SN lead time, SN total benefits, etc.)

Determines the optimum solution by collaborative decision making


Literature Review (I)


The basic structure of a conventional BOM has always been to

relate a parent item with one or several child items, which only

takes place in pure convergent product structures

The matrix linking each parent item with its child items required

for its assembly appears in this formulation according to the

Gozinto structure presented by Vazsonyi (1954)

The conventional way of representing the BOM is the Gozinto

Matrix (goes into) Aij in which products i (parent item) relate to

products j (child items)

In association with each product i, the quantity of resource r

required to produce a unit of product i by means of matrix Uir is

also constituted, and this structure was considered by (Mize et al.,

1982)

Literature Review (II)

Mathematical formulation of the MRP &

MRPII: Vazsonyi (1954), Mize, White & Brooks

(1982), Billington (1983), Staedtler(1993)

Alternative products and resources

CMIT and the alternate BOM: Escudero (1994)

RPS: Balakrishnan and Geunes (2000) Geunes (2003)

Lang y Domschke (2010)

Flexible BOM: Ram et al (2006)

Product Binning: Lin et al (2009)

Reverse Bill of Materials

Divergents structures: Segersted (1996)

Reverse MRP (Gupta y Taleb, 1994)

Alternative dismantling process (Spengler et al 1997)

Reverse LSP (Barba-Gutierrez et al, 2008)

BOM y rBOM: Schutz (2009)

Severals Inputs and outputs in the same

process

STN: Pantelides (1994)

RTN: Barbosa y Pantelides (1997)

Deliberated Co-producction (Vidal-Garcia et al. 2012)

Stroke (Maheut & Garcia-Sabater 2011; Maheut et al.

2012; Maheut and Garcia-Sabater 2012; Garcia-Sabater

et al. 2013)

Transport between plants

BOM y rBOM: Schutz (2009)

STN (Sousa et al., 2008)

Transport as an operation (Pires et al, 2008)

Packagings

Voss y Woodruff (2005)

Pinto et al. (2007)

Lang & Domschke (2010)


Outline

I. Introduction








V. Some results



The “Stroke” concept Each stroke corresponds to a specific located operation. It is characterised by the use of located

resources (Garcia-Sabater et al. 2012; Garcia-Sabater et al. 2013; Maheut et al. 2012; Maheut & Garcia-

Sabater 2011)

Lead times, setup times and costs, time consumption and the costs of performing one stroke unit are

assigned to the stroke and not to the result of the operation.

Resources are associated with each stroke, but not with the product (or the series of products)

obtained.


Outline

I. Introduction








V. Some results



The DSS –

Brief Introduction


Spanish SME that designs, manufactures, transports, installs customized machine tool,

specifically milling machines and milling centres in Europe

Number of product variants theoretically includes around 2,5 billions possible combinations

The DSS –

Brief Introduction


SimulationSimulation

SNI1

SNI2

SNI3

Predefined

Strategies

SN Model Define Supply Strategies Simulation KPIs

SN Model Optimize Order SNI KPIs

Optimal

SNI

OptimizationOptimization and and SimulationSimulation

C1

C2

C1: Cost Cn C2: Delivery Time OptimizationOptimization

CriteriaCriteria

The DSS – Brief Introduction -A Tool

for Collaborative Decision Making


Centralized Coordinator

DSS

Plant 1 Plant 2 Plant n

The DSS – A Data Base enable

alternative operations


The DSS –

Optimization Tool


The DSS –

Optimization Tool - Steps

I. Transformation

I. Generation of the Stroke Graph with the Data Base

II. Incorporation of Transport Operations between Locations

III. Transformation of the Stroke Graph into an Hybrid Stroke Graph incorporating Selection

Strokes

IV. Transformation into an Arcs-Nodes Hyper-graph

II. Algorithm for Complete enumeration

I. Generation of all feasible solutions

II. Assessment in cost, lead time, operations schedule of all feasible solution

III. Selection of the chosen solutions by the Coordinator

IV. Simulations for complete evaluation by the Simulation Tool

V. Selection by the Supply Network Coordinator of the Suitable Supply Network

Instance based on multi-criteria decision


The DSS –

Optimization Tool Generation of the Stroke Graph


The DSS –

Optimization Tool Incorporating Transport Strokes between locations


The DSS –

Optimization Tool Incorporating Selection Strokes


The DSS –

Optimization Tool Generating all the feasible solutions


• 2n solutions, initially. ‘n’ = number of selection arcs

• Delete 0-0-0-0 solution

• Delete solutions with number of active arcs > m

m = number of selection nodes

• Delete solutions with more than one arc active in a

selection node

• For each solution:

• Activate the rest of arcs and nodes

• Update the graph:

• An arc not activated, deactivates his initial node.

• A node not activated, deactivates his entry

and his final node (not selection arcs)

arcs and his outing arc

• If the root node is not actived, the solution is not

possible.

• Delete redundant solutions

The DSS –

Optimization Tool

For each solution

Calculate earliness beginning and ending for each node and arc

Calculate tardiness beginning and ending for each node and arc

Calculate time needed to supply the order

Find the associated strokes that must be performed

The Coordinator Agent chooses the solution to assess in the Simulation Tool

Then the solution that satisfices the stakeholders is performed


The DSS –

The simulation tool


Outline

I. Introduction








V. Some results



Some results

Bimatec – Soraluce case study


Simulation and Optimization KPIs

Conclusions & further works

A DSS to solve the SN configuration problem and the operations scheduling

problem of a company that assembles highly customised machine tools in several

European plants has been described.

A novel database structure that is able to consider alternative operations

(purchasing, production, routing and transport) and alternative BOMs (upgrading,

reconfiguring custom products) has been introduced.

The steps of an algorithm for complete enumeration to determine all the feasible

solutions have been presented.

Then a simulator based on multi-agent technology evaluates the different KPIs by

collaborative decision making.


Thank your for your attention

Any Question?

Julien maheut

PhD Candidat at Research Group ROGLE

Universidad Politécnica de Valencia (Spain)


The research leading to these results has received funding from the European Community's Seventh Framework

Programme (FP7/2007-2013) under grant agreement no. NMP2-SL-2009- 229333 and has been partially

supported by the Spanish Ministry of Science and Innovation within the "Proyectos de Investigación Fundamental

No Orientada Programme through Project "CORSARI MAGIC DPI2010-18243".

decision support system prototype for supply network ...decision support system prototype for supply...

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