Metaheuristics for production scheduling

著者

    • Jarboui, Bassem
    • Siarry, Patrick
    • Teghem, Jacques

書誌事項

Metaheuristics for production scheduling

edited by Bassem Jarboui, Patrick Siarry, Jacques Teghem

(Automation-control and industrial engineering series)

ISTE , Wiley, 2013

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注記

Includes bibliography and index

内容説明・目次

内容説明

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 Rebai. 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 Rebai. 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags, Emna Dhouib, Jacques Teghem, Daniel Tuyttens and Taicir Loukil. 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search, Marie-Eleonore 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, Said 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 Gagne, 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, Tiente Hsu, Gilles Goncalves and Remy Dupas. 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities, Virginie Andre, Nathalie Grangeon and Sylvie Norre. 16. Vehicle Routing Problems with Scheduling Constraints, Rahma Lahyani, Frederic Semet and Benoit Trouillet. 17. Metaheuristics for Job Shop Scheduling with Transportation, Qiao Zhang, Herve 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 Systemes Intelligents (LISSI), University of Paris-Est Creteil, France. Jacques Teghem is Professor at the University of Mons, Belgium.

目次

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 REBAI 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 REBAI 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 Taicir 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-Eleonore 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, Said 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 GAGNE, 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 Tiente HSU, Gilles GONCALVES and Remy 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 ANDRE, 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, Frederic SEMET and Benoit 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, Herve 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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詳細情報

  • NII書誌ID(NCID)
    BB15797596
  • ISBN
    • 9781848214972
  • 出版国コード
    uk
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    London,Hoboken, N.J.
  • ページ数/冊数
    xxvi, 501 p.
  • 大きさ
    24 cm
  • 親書誌ID
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