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.
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
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