About the Book
This book describes the potentialities of metaheuristics for solving production scheduling problems and the relationship between these two fields.
For the past several years, there has been an increasing interest in using metaheuristic methods to solve scheduling problems. The main reasons for this are that such problems are generally hard to solve to optimality, as well as the fact that metaheuristics provide very good solutions in a reasonable time. The first part of the book presents eight applications of metaheuristics for solving various mono-objective scheduling problems. The second part is itself split into two, the first section being devoted to five multi-objective problems to which metaheuristics are adapted, while the second tackles various transportation problems related to the organization of production systems.
Many real-world applications are presented by the authors, making this an invaluable resource for researchers and students in engineering, economics, mathematics and computer science.
Contents
1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times, Mansour Eddaly, Bassem Jarboui, Radhouan Bouabda, Patrick Siarry and Abdelwaheb Rebaï.
2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems, Imed Kacem.
3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints, Hanen Akrout, Bassem Jarboui, Patrick Siarry and Abdelwaheb Rebaï.
4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags, Emna Dhouib, Jacques Teghem, Daniel Tuyttens and Taïcir Loukil.
5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search, Marie-Eléonore Marmion.
6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints, Nadia Chaaben, Racem Mellouli and Faouzi Masmoudi.
7. Models and Methods in Graph Coloration for Various Production Problems, Nicolas Zufferey.
8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties, Mustapha Ratli, Rachid Benmansour, Rita Macedo, Saïd Hanafi, Christophe Wilbaut.
9. Metaheuristics for Biobjective Flow Shop Scheduling, Matthieu Basseur and Arnaud Liefooghe.
10. Pareto Solution Strategies for the Industrial Car Sequencing Problem, Caroline Gagné, Arnaud Zinflou and Marc Gravel.
11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance, Ali Berrichi and Farouk Yalaoui.
12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling, Fouzia Ounnar, Patrick Pujo and Afef Denguir.
13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem, Olfa Dridi, Saoussen Krichen and Adel Guitouni.
14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context, Tienté Hsu, Gilles Gonçalves and Rémy Dupas.
15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities, Virginie André, Nathalie Grangeon and Sylvie Norre.
16. Vehicle Routing Problems with Scheduling Constraints, Rahma Lahyani, Frédéric Semet and Benoît Trouillet.
17. Metaheuristics for Job Shop Scheduling with Transportation, Qiao Zhang, Hervé Manier, Marie-Ange Manier.
About the Authors
Bassem Jarboui is Professor at the University of Sfax, Tunisia.
Patrick Siarry is Professor at the Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), University of Paris-Est Créteil, France.
Jacques Teghem is Professor at the University of Mons, Belgium.
Table of Contents:
Introduction and Presentation xv
Bassem JARBOUI, Patrick SIARRY and Jacques TEGHEM
Chapter 1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times 1
Mansour EDDALY, Bassem JARBOUI, Radhouan BOUABDA, Patrick SIARRY and Abdelwaheb REBAÏ
1.1. Introduction 1
1.2. Mathematical formulation 3
1.3. Estimation of distribution algorithms 5
1.3.1. Estimation of distribution algorithms proposed in the literature 6
1.4. The proposed estimation of distribution algorithm 8
1.4.1. Encoding scheme and initial population 8
1.4.2. Selection 9
1.4.3. Probability estimation 9
1.5. Iterated local search algorithm 10
1.6. Experimental results 11
1.7. Conclusion 15
1.8. Bibliography 15
Chapter 2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems 19
Imed KACEM
2.1. Introduction 19
2.2. Flexible job shop scheduling problems 19
2.3. Genetic algorithms for some related sub-problems 25
2.4. Genetic algorithms for the flexible job shop problem 31
2.4.1. Codings 31
2.4.2. Mutation operators 34
2.4.3. Crossover operators 38
2.5. Comparison of codings 42
2.6. Conclusion 43
2.7. Bibliography 43
Chapter 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints 45
Hanen AKROUT, Bassem JARBOUI, Patrick SIARRY and Abdelwaheb REBAÏ
3.1. Introduction 45
3.2. Overview of the literature 47
3.2.1. Single-solution metaheuristics 47
3.2.2. Population-based metaheuristics 49
3.2.3. Hybrid approaches 49
3.3. Description of the problem 50
3.4. GRASP 52
3.5. Differential evolution 53
3.6. Iterative local search 55
3.7. Overview of the NEW-GRASP-DE algorithm 55
3.7.1. Constructive phase 56
3.7.2. Improvement phase 57
3.8. Experimental results 57
3.8.1. Experimental results for the Reeves and Heller instances 58
3.8.2. Experimental results for the Taillard instances 60
3.9. Conclusion 62
3.10. Bibliography 64
Chapter 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags 69
Emna DHOUIB, Jacques TEGHEM, Daniel TUYTTENS and Taïcir LOUKIL
4.1. Introduction 69
4.2. Description of the problem 70
4.2.1. Flowshop with time lags 70
4.2.2. A bicriteria hierarchical flow shop problem 71
4.3. The proposed metaheuristics 73
4.3.1. A simulated annealing metaheuristics 74
4.3.2. The GRASP metaheuristics 77
4.4. Tests 82
4.4.1. Generated instances 82
4.4.2. Comparison of the results 83
4.5. Conclusion 94
4.6. Bibliography 94
Chapter 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search 97
Marie-Eléonore MARMION
5.1. Introduction 97
5.2. Neutrality in a combinatorial optimization problem 98
5.2.1. Landscape in a combinatorial optimization problem 99
5.2.2. Neutrality and landscape 102
5.3. Study of neutrality in the flow shop problem 106
5.3.1. Neutral degree 106
5.3.2. Structure of the neutral landscape 108
5.4. Local search exploiting neutrality to solve the flow shop problem 112
5.4.1. Neutrality-based iterated local search 113
5.4.2. NILS on the flow shop problem 116
5.5. Conclusion 122
5.6. Bibliography 123
Chapter 6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints 127
Nadia CHAABEN, Racem MELLOULI and Faouzi MASMOUDI
6.1. Introduction 127
6.2. Overview of the literature 128
6.3. Overview of the problem and notations used 131
6.4. Mathematical formulations 133
6.4.1. First formulation (MILP1) 133
6.4.2. Second formulation (MILP2) 135
6.4.3. Third formulation (MILP3) 137
6.5. A genetic algorithm: model and methodology 139
6.5.1. Coding used for our algorithm 139
6.5.2. Generating the initial population 140
6.5.3. Selection operator 142
6.5.4. Crossover operator 142
6.5.5. Mutation operator 144
6.5.6. Insertion operator 144
6.5.7. Evaluation function: fitness 144
6.5.8. Stop criterion 145
6.6. Verification and validation of the genetic algorithm 145
6.6.1. Description of benchmarks 145
6.6.2. Tests and results 146
6.7. Conclusion 148
6.8. Bibliography 148
Chapter 7. Models and Methods in Graph Coloration for Various Production Problems 153
Nicolas ZUFFEREY
7.1. Introduction 153
7.2. Minimizing the makespan 155
7.2.1. Tabu algorithm 155
7.2.2. Hybrid genetic algorithm 157
7.2.3. Methods prior to GH 158
7.2.4. Extensions 159
7.3. Maximizing the number of completed tasks 160
7.3.1. Tabu algorithm 161
7.3.2. The ant colony algorithm 162
7.3.3. Extension of the problem 164
7.4. Precedence constraints 165
7.4.1. Tabu algorithm 168
7.4.2. Variable neighborhood search method 169
7.5. Incompatibility costs 171
7.5.1. Tabu algorithm 173
7.5.2. Adaptive memory method 175
7.5.3. Variations of the problem 177
7.6. Conclusion 178
7.7. Bibliography 179
Chapter 8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties 183
Mustapha RATLI, Rachid BENMANSOUR, Rita MACEDO, Saïd HANAFI, Christophe WILBAUT
8.1. Introduction 183
8.2. Properties and particular cases 185
8.3. Mathematical models 188
8.3.1. Linear models with precedence variables 188
8.3.2. Linear models with position variables 192
8.3.3. Linear models with time-indexed variables 194
8.3.4. Network flow models 197
8.3.5. Quadratic models 197
8.3.6. A comparative study 199
8.4. Heuristics 203
8.4.1. Properties 207
8.4.2. Evaluation 209
8.5. Metaheuristics 211
8.6. Conclusion 217
8.7. Acknowledgments 218
8.8. Bibliography 218
Chapter 9. Metaheuristics for Biobjective Flow Shop Scheduling 225
Matthieu BASSEUR and Arnaud LIEFOOGHE
9.1. Introduction 225
9.2. Metaheuristics for multiobjective combinatorial optimization 226
9.2.1. Main concepts 227
9.2.2. Some methods 229
9.2.3. Performance analysis 232
9.2.4. Software and implementation 237
9.3. Multiobjective flow shop scheduling problems 238
9.3.1. Flow shop problems 239
9.3.2. Permutation flow shop with due dates 240
9.3.3. Different objective functions 241
9.3.4. Sets of data 241
9.3.5. Analysis of correlations between objectives functions 242
9.4. Application to the biobjective flow shop 243
9.4.1. Model 244
9.4.2. Solution methods 246
9.4.3. Experimental analysis 246
9.5. Conclusion 249
9.6. Bibliography 250
Chapter 10. Pareto Solution Strategies for the Industrial Car Sequencing Problem 253
Caroline GAGNÉ, Arnaud ZINFLOU and Marc GRAVEL
10.1. Introduction 253
10.2. Industrial car sequencing problem 255
10.3. Pareto strategies for solving the CSP 260
10.3.1. PMSMO 260
10.3.2. GISMOO 264
10.4. Numerical experiments 268
10.4.1. Test sets 269
10.4.2. Performance metrics 270
10.5. Results and discussion 271
10.6. Conclusion 279
10.7. Bibliography 280
Chapter 11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance 283
Ali BERRICHI and Farouk YALAOUI
11.1. Introduction 283
11.2. State of the art on the joint problem 285
11.3. Integrated modeling of the joint problem 287
11.4. Concepts of multi-objective optimization 291
11.5. The particle swarm optimization method 292
11.6. Implementation of MOPSO algorithms 294
11.6.1. Representation and construction of the solutions 294
11.6.2. Solution Evaluation 295
11.6.3. The proposed MOPSO algorithms 298
11.6.4. Updating the velocities and positions 299
11.6.5. Hybridization with local searches 300
11.7. Experimental results 302
11.7.1. Choice of test problems and configurations 302
11.7.2. Experiments and analysis of the results 303
11.8. Conclusion 310
11.9. Bibliography 311
Chapter 12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling 315
Fouzia OUNNAR, Patrick PUJO and Afef DENGUIR
12.1. Introduction 315
12.2. Methods for solving multicriteria scheduling 316
12.2.1. Optimization methods 316
12.2.2. Multicriteria decision aid methods 318
12.2.3. Choice of the multicriteria decision aid method 319
12.3. Presentation of the AHP method 320
12.3.1. Phase 1: configuration 320
12.3.2. Phase 2: exploitation 321
12.4. Evaluation of metaheuristics for the configuration of AHP 322
12.4.1. Local search methods 323
12.4.2. Population-based methods 324
12.4.3. Advanced metaheuristics 326
12.5. Choice of metaheuristic 326
12.5.1. Justification of the choice of genetic algorithms 326
12.5.2. Genetic algorithms 328
12.6. AHP optimization by a genetic algorithm 330
12.6.1. Phase 0: configuration of the structure of the problem 331
12.6.2. Phase 1: preparation for automatic configuration 332
12.6.3. Phase 2: automatic configuration 334
12.6.4. Phase 3: preparation of the exploitation phase 335
12.7. Evaluation of G-AHP 336
12.7.1. Analysis of the behavior of G-AHP 336
12.7.2. Analysis of the results obtained by G-AHP 342
12.8. Conclusions 343
12.9. Bibliography 344
Chapter 13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem 349
Olfa DRIDI, Saoussen KRICHEN and Adel GUITOUNI
13.1. Introduction 349
13.2. Description and formulation of the problem 350
13.3. Literature review 353
13.3.1. Exact methods 354
13.3.2. Approximate methods 355
13.4. A multicriteria genetic algorithm for the MMSAP 356
13.4.1. Encoding variables 357
13.4.2. Genetic operators 358
13.4.3. Parameter settings 359
13.4.4. The GA 360
13.5. Experimental study 361
13.5.1. Diversification of the approximation set based on the diversity indicators 364
13.6. Conclusion 369
13.7. Bibliography 369
Chapter 14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context 373
Tienté HSU, Gilles GONÇALVES and Rémy DUPAS
14.1. Introduction 373
14.2. Dynamic vehicle route management 375
14.2.1. The vehicle routing problem with time windows 377
14.3. Platform for the solution of the DVRPTW 382
14.3.1. Encoding a chromosome 384
14.4. Treating uncertainties in the orders 386
14.5. Treatment of traffic information 392
14.6. Conclusion 397
14.7. Bibliography 398
Chapter 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities 401
Virginie ANDRÉ, Nathalie GRANGEON and Sylvie NORRE
15.1. Knowledge model 403
15.1.1. Fixed resources and mobile resources 403
15.1.2. Modelling the activities in steps 404
15.1.3. The problem to be solved 406
15.1.4. Illustrative example 407
15.2. Solution procedure 410
15.3. Proposed approach 413
15.3.1. Metaheuristics 414
15.3.2. Simulation model 421
15.4. Implementation and results 422
15.4.1. Impact on the work mode 423
15.4.2. Results of the set of modifications to the teaching hospital 425
15.4.3. Preliminary study of the choice of shifts 428
15.5. Conclusion 430
15.6. Bibliography 431
Chapter 16. Vehicle Routing Problems with Scheduling Constraints 433
Rahma LAHYANI, Frédéric SEMET and Benoît TROUILLET
16.1. Introduction 433
16.2. Definition, complexity and classification 435
16.2.1. Definition and complexity 435
16.2.2. Classification 436
16.3. Time-constrained vehicle routing problems 438
16.3.1. Vehicle routing problems with time windows 438
16.3.2. Period vehicle routing problems 441
16.3.3. Vehicle routing problem with cross-docking 443
16.4. Vehicle routing problems with resource availability constraints 448
16.4.1. Multi-trip vehicle routing problem 448
16.4.2. Vehicle routing problem with crew scheduling 450
16.5. Conclusion 452
16.6. Bibliography 453
Chapter 17. Metaheuristics for Job Shop Scheduling with Transportation 465
Qiao ZHANG, Hervé MANIER, Marie-Ange MANIER
17.1. General flexible job shop scheduling problems 466
17.2. State of the art on job shop scheduling with transportation resources 468
17.3. GTSB procedure 474
17.3.1. A hybrid metaheuristic algorithm for the GFJSSP 474
17.3.2. Tests and results 480
17.3.3. Conclusion for GTSB 489
17.4. Conclusion 491
17.5. Bibliography 491
List of Authors 495
Index 499
