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.

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