Metaheuristics for Production Scheduling [NOOK Book]

Overview

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 ...

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Metaheuristics for Production Scheduling

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Overview

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 Universit

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Product Details

  • ISBN-13: 9781118731567
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 6/12/2013
  • Series: ISTE
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • File size: 11 MB
  • Note: This product may take a few minutes to download.

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

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