Production Planning in Automated Manufacturing

234 Pages·1996·6.732 MB·English

by  Dr. Yves Crama, Dr. Alwin G. Oerlemans, Dr. Frits C. R. Spieksma (auth.)

Production Planning in
Automated Manufacturing
Second, Revised and Enlarged Edition
Springer
Berlin
Heidelberg
New York
Barcelona
Budapest
Hong Kong
London
Milan
Paris
Santa Clara
Singapore
Tokyo
Yves Crama . Alwin G. Oerlemans . Frits C. R. Spieksma
Production Planning in
Automated Manufacturing
Second, Revised and Enlarged Edition
With 7 Figures
, Springer
Dr. Yves Crama
Universi~ de Liege
Facult~ d'Economie, de Gestion
et de Sciences Sociales
Boulevard du Rectorat 7 (B31)
B-4000 Liege, Belgium
Dr. Alwin G. Oerlemans
Ministerie van Financien
Korte Voorhout 7
NL-z511 CW The Hague, The Netherlands
Dr. Frits C. R. Spieksma
Rijksuniversiteit Limburg
Department of Mathematics
P. O. Box 616
NL-62.o0 MD Maastricht, The Netherlands
Cataloging-in-Publication Data applied for
Die Deutsche Bibliothek - CIP-Einheitsaurnahme
Crama, Yves:
Production planning in automated manuracturing : with 40
tables I Yves Crama ; Alwin G. Oerlemans ; Frits C. R.
Spieksma. - 2 .• rev. and ent. ed. - Berlin; Heidelberg; New
York; Barcelona; Budapest; Hong Kong; London; Milan;
Paris; Santa Clara; Singapore; Tokyo: Springer. 1996
ISBN -13:978-3-642-80272-0
NE: Oerlemans. Alwin:; Spiebma. Frits:
ISBN-13:978-3-642-80272-0 e-ISBN-13:978-3-642-80270-6
001: 10.1°°7/978-3-642-8027°-6
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Preface
This monograph is based on the theses ofOerlemans (1992) and Spieksma
(1992). In this second edition a new chapter (Chapter 5) is added which in
vestigates basic models for tool-loading problems. Further, we have revised
and updated the other chapters. We would like to thank the many individ
uals, at the University of Limburg or elsewhere, who have contributed to
the genesis of this work. We are especially indebted to Antoon Kolen, who
co-authored Chapters 2 and 9, and who delivered numerous comments on
all other parts of the monograph. We also want to thank Koos Vrieze and
Hans-Jiirgen Bandelt for their constructive remarks.
Contents
Preface v
Contents vii
1 Automated manufacturing 1
1.1 Introduction.... .... 3
1.2 Production planning for FMSs 4
1.2.1 What is an FMS? ... 4
1.2.2 The hierarchical approach 5
1.2.3 Tactical Planning ... 7
1.2.4 Operational planning. 10
1.3 Overview of the monograph . 10
2 Throughput rate optimization in the automated assembly of
printed circuit boards 17
2.1 Introduction.................. 19
2.2 Technological environment . . . . . . . . . . 20
2.3 The throughput rate optimization problem 23
2.4 Workload balancing .. 25
2.4.1 Subproblem (A) ...... 25
2.4.2 Subproblem (B) ...... 29
2.5 Scheduling of individual machines. 31
2.5.1 Subproblem (C) 32
2.5.2 Subproblem (D) . 34
2.5.3 Subproblem (E) 37
2.5.4 Subproblem (F) . 39
2.6 An example . . . . . . . 42
viii Contents
3 Approximation algorithms for three-dimensional assignment
problems with triangle inequalities 47
3.1 Introduction......... 49
3.2 Complexity of T fl. and S fl. 50
3.3 Approximation algorithms . 52
3.4 Computational results . . . 59
4 Scheduling jobs of equal length: complexity, facets and
computational results 63
4.1 Introduction........ 65
4.2 Complexity of SEL . . . . 67
4.3 The LP-relaxation of SEL 69
4.4 More facet-defining and valid inequalities for SEL . 79
4.5 A cutting-plane algorithm for SEL . . . . . . . . . 86
5 The tool loading problem: an overview 91
5.1 Introduction................ 93
5.2 Machine flexibility and tool management. . 93
5.3 Modeling the magazine capacity constraint 95
5.3.1 A linear model . . . . . . . . 95
5.3.2 Nonlinear models . . . . . . . 97
5.4 Solving the batch selection problem. 98
5.5 Grouping of parts and tools 100
5.6 Tool switching ............ 102
6 A column generation approach to job grouping 107
6.1 Introduction............. 109
6.2 Lower bounds. . . . . . . . . . . . 110
6.2.1 The job grouping problem . 110
6.2.2 Column generation . . . . . 112
6.2.3 The generation subproblem 114
6.2.4 Computation of lower bounds via column generation 116
6.2.5 Lagrangian relaxation 118
6.2.6 Other lower bounds ........ 120
6.3 Upper bounds ............... . 121
6.3.1 Sequential heuristics for grouping. 122
6.3.2 Set covering heuristics 123
6.4 Implementation....... 124
6.5 Computational experiments . 127
ix
6.5.1 Generation of problem instances 127
6.5.2 Computational results 129
6.6 Summary and conclusions . . . . . . . . 137
7 The job grouping problem for flexible manufacturing
systems: some extensions 139
7.1 Introduction ............ . 141
7.2 Multipleslots ........... . 141
7.2.1 The job grouping problem. 141
7.2.2 Lower bounds via column generation. 143
7.2.3 Other lower bounds ......... . 144
7.2.4 Upper bounds ............. . 145
7.2.5 Adjusting the column generation procedure 146
7.2.6 Computational experiments. 148
7.2.7 Computational results ... . 149
7.3 Multiple machines ......... . 158
7.3.1 The job grouping problem .. 158
7.3.2 Lower bounds via column generation. 158
7.3.3 Other lower bounds ......... . 159
7 .3.4 Upper bounds. . . . . . . . . . . . . . 160
7.3.5 Adjusting the column generation procedure 160
7.3.6 Computational experiments 161
7.3.7 Computational results 162
7.4 Other extensions . . . . . 168
7.5 Summary and conclusions . . 169
8 A local search approach to job grouping 171
8.1 Introduction ....... . 173
8.2 Local search environment 174
8.2.1 Starting solution 175
8.2.2 Objective function 176
8.2.3 Neighbourhood structure 177
8.2.4 Stopping criteria . . . . . 177
8.3 Local search approaches . . . . . 178
8.3.1 Simple improvement approach 178
8.3.2 Tabu search approach . . . . . 178
8.3.3 Simulated annealing approach. 179
8.3.4 Variable-depth approach . 180
8.4 Computational experiments . . . . . . 181
x Contents
8.4.1 The dataset ..... . 181
8.4.2 Computational results 183
8.5 Summary and conclusions .. 188
9 Minimizing the number of tool switches on a flexible
machine 191
9.1 Introduction......... 193
9.2 Basic results. . . . . . . . . 196
9.2.1 NP-hardness results 196
9.2.2 Finding the minimum number of setups for a fixed job
sequence ........... . 198
9.3 Heuristics .............. . 202
9.3.1 Traveling salesman heuristics 202
9.3.2 Block minimization heuristics 204
9.3.3 Greedy heuristics 205
9.3.4 Interval heuristic . . . . . .. 206
9.3.5 2-0pt strategies ..... .. 207
9.3.6 Load-and-Optimize strategy. 208
9.4 Computational experiments . . . .. 209
9.4.1 Generation of problem instances 209
9.4.2 Computational results . . 210
9.5 Lower bounds .............. . 216
9.5.1 Traveling salesman paths ... . 216
9.5.2 Structures implying extra setups 217
9.5.3 Valid inequalities ..... 219
9.5.4 Lagrangian relaxation .. 220
Appendix: Graph-theoretic definitions 222
References 225
Chapter 1
Automated manufacturing