Metaheuristics for Production Scheduling

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

Bassem Jarboui, Laboratoire MODILS, University of Sfax, Tunisia.

Patrick Siarry, Laboratoire LiSSi, University of Paris-Est Créteil, France.

Jacques Teghem, MathRO / Polytechnic Faculty of Mons, Belgium.