Cover -- Title Page -- Contents -- Introduction and Presentation -- Chapter 1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times -- 1.1. Introduction -- 1.2. Mathematical formulation -- 1.3. Estimation of distribution algorithms -- 1.3.1. Estimation of distribution algorithms proposed in the literature -- 1.4. The proposed estimation of distribution algorithm -- 1.4.1. Encoding scheme and initial population -- 1.4.2. Selection -- 1.4.3. Probability estimation -- 1.5. Iterated local search algorithm -- 1.6. Experimental results -- 1.7. Conclusion -- 1.8. Bibliography -- Chapter 2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems -- 2.1. Introduction -- 2.2. Flexible job shop scheduling problems -- 2.3. Genetic algorithms for some related sub-problems -- 2.4. Genetic algorithms for the flexible job shop problem -- 2.4.1. Codings -- 2.4.2. Mutation operators -- 2.4.3. Crossover operators -- 2.5. Comparison of codings -- 2.6. Conclusion -- 2.7. Bibliography -- Chapter 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints -- 3.1. Introduction -- 3.2. Overview of the literature -- 3.2.1. Single-solution metaheuristics -- 3.2.2. Population-based metaheuristics -- 3.2.3. Hybrid approaches -- 3.3. Description of the problem -- 3.4. GRASP -- 3.5. Differential evolution -- 3.6. Iterative local search -- 3.7. Overview of the NEW-GRASP-DE algorithm -- 3.7.1. Constructive phase -- 3.7.2. Improvement phase -- 3.8. Experimental results -- 3.8.1. Experimental results for the Reeves and Heller instances -- 3.8.2. Experimental results for the Taillard instances -- 3.9. Conclusion -- 3.10. Bibliography.

Chapter 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags -- 4.1. Introduction -- 4.2. Description of the problem -- 4.2.1. Flowshop with time lags -- 4.2.2. A bicriteria hierarchical flow shop problem -- 4.3. The proposed metaheuristics -- 4.3.1. A simulated annealing metaheuristics -- 4.3.2. The GRASP metaheuristics -- 4.4. Tests -- 4.4.1. Generated instances -- 4.4.2. Comparison of the results -- 4.5. Conclusion -- 4.6. Bibliography -- Chapter 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search -- 5.1. Introduction -- 5.2. Neutrality in a combinatorial optimization problem -- 5.2.1. Landscape in a combinatorial optimization problem -- 5.2.2. Neutrality and landscape -- 5.3. Study of neutrality in the flow shop problem -- 5.3.1. Neutral degree -- 5.3.2. Structure of the neutral landscape -- 5.4. Local search exploiting neutrality to solve the flow shop problem -- 5.4.1. Neutrality-based iterated local search -- 5.4.2. NILS on the flow shop problem -- 5.5. Conclusion -- 5.6. Bibliography -- Chapter 6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints -- 6.1. Introduction -- 6.2. Overview of the literature -- 6.3. Overview of the problem and notations used -- 6.4. Mathematical formulations -- 6.4.1. First formulation (MILP1) -- 6.4.2. Second formulation (MILP2) -- 6.4.3. Third formulation (MILP3) -- 6.5. A genetic algorithm: model and methodology -- 6.5.1. Coding used for our algorithm -- 6.5.2. Generating the initial population -- 6.5.3. Selection operator -- 6.5.4. Crossover operator -- 6.5.5. Mutation operator -- 6.5.6. Insertion operator -- 6.5.7. Evaluation function: fitness -- 6.5.8. Stop criterion -- 6.6. Verification and validation of the genetic algorithm.

6.6.1. Description of benchmarks -- 6.6.2. Tests and results -- 6.7. Conclusion -- 6.8. Bibliography -- Chapter 7. Models and Methods in Graph Coloration for Various Production Problems -- 7.1. Introduction -- 7.2. Minimizing the makespan -- 7.2.1. Tabu algorithm -- 7.2.2. Hybrid genetic algorithm -- 7.2.3. Methods prior to GH -- 7.2.4. Extensions -- 7.3. Maximizing the number of completed tasks -- 7.3.1. Tabu algorithm -- 7.3.2. The ant colony algorithm -- 7.3.3. Extension of the problem -- 7.4. Precedence constraints -- 7.4.1. Tabu algorithm -- 7.4.2. Variable neighborhood search method -- 7.5. Incompatibility costs -- 7.5.1. Tabu algorithm -- 7.5.2. Adaptive memory method -- 7.5.3. Variations of the problem -- 7.6. Conclusion -- 7.7. Bibliography -- Chapter 8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties -- 8.1. Introduction -- 8.2. Properties and particular cases -- 8.3. Mathematical models -- 8.3.1. Linear models with precedence variables -- 8.3.2. Linear models with position variables -- 8.3.3. Linear models with time-indexed variables -- 8.3.4. Network flow models -- 8.3.5. Quadratic models -- 8.3.6. A comparative study -- 8.4. Heuristics -- 8.4.1. Properties -- 8.4.2. Evaluation -- 8.5. Metaheuristics -- 8.6. Conclusion -- 8.7. Acknowledgments -- 8.8. Bibliography -- Chapter 9. Metaheuristics for Biobjective Flow Shop Scheduling -- 9.1. Introduction -- 9.2. Metaheuristics for multiobjective combinatorial optimization -- 9.2.1. Main concepts -- 9.2.2. Some methods -- 9.2.3. Performance analysis -- 9.2.4. Software and implementation -- 9.3. Multiobjective flow shop scheduling problems -- 9.3.1. Flow shop problems -- 9.3.2. Permutation flow shop with due dates -- 9.3.3. Different objective functions -- 9.3.4. Sets of data -- 9.3.5. Analysis of correlations between objectives functions.

9.4. Application to the biobjective flow shop -- 9.4.1. Model -- 9.4.2. Solution methods -- 9.4.3. Experimental analysis -- 9.5. Conclusion -- 9.6. Bibliography -- Chapter 10. Pareto Solution Strategies for the Industrial Car Sequencing Problem -- 10.1. Introduction -- 10.2. Industrial car sequencing problem -- 10.3. Pareto strategies for solving the CSP -- 10.3.1. PMSMO -- 10.3.2. GISMOO -- 10.4. Numerical experiments -- 10.4.1. Test sets -- 10.4.2. Performance metrics -- 10.5. Results and discussion -- 10.6. Conclusion -- 10.7. Bibliography -- Chapter 11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance -- 11.1. Introduction -- 11.2. State of the art on the joint problem -- 11.3. Integrated modeling of the joint problem -- 11.4. Concepts of multi-objective optimization -- 11.5. The particle swarm optimization method -- 11.6. Implementation of MOPSO algorithms -- 11.6.1. Representation and construction of the solutions -- 11.6.2. Solution Evaluation -- 11.6.3. The proposed MOPSO algorithms -- 11.6.4. Updating the velocities and positions -- 11.6.5. Hybridization with local searches -- 11.7. Experimental results -- 11.7.1. Choice of test problems and configurations -- 11.7.2. Experiments and analysis of the results -- 11.8. Conclusion -- 11.9. Bibliography -- Chapter 12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling -- 12.1. Introduction -- 12.2. Methods for solving multicriteria scheduling -- 12.2.1. Optimization methods -- 12.2.2. Multicriteria decision aid methods -- 12.2.3. Choice of the multicriteria decision aid method -- 12.3. Presentation of the AHP method -- 12.3.1. Phase 1: configuration -- 12.3.2. Phase 2: exploitation -- 12.4. Evaluation of metaheuristics for the configuration of AHP -- 12.4.1. Local search methods.

12.4.2. Population-based methods -- 12.4.3. Advanced metaheuristics -- 12.5. Choice of metaheuristic -- 12.5.1. Justification of the choice of genetic algorithms -- 12.5.2. Genetic algorithms -- 12.6. AHP optimization by a genetic algorithm -- 12.6.1. Phase 0: configuration of the structure of the problem -- 12.6.2. Phase 1: preparation for automatic configuration -- 12.6.3. Phase 2: automatic configuration -- 12.6.4. Phase 3: preparation of the exploitation phase -- 12.7. Evaluation of G-AHP -- 12.7.1. Analysis of the behavior of G-AHP -- 12.7.2. Analysis of the results obtained by G-AHP -- 12.8. Conclusions -- 12.9. Bibliography -- Chapter 13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem -- 13.1. Introduction -- 13.2. Description and formulation of the problem -- 13.3. Literature review -- 13.3.1. Exact methods -- 13.3.2. Approximate methods -- 13.4. A multicriteria genetic algorithm for the MMSAP -- 13.4.1. Encoding variables -- 13.4.2. Genetic operators -- 13.4.3. Parameter settings -- 13.4.4. The GA -- 13.5. Experimental study -- 13.5.1. Diversification of the approximation set based on the diversity indicators -- 13.6. Conclusion -- 13.7. Bibliography -- Chapter 14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context -- 14.1. Introduction -- 14.2. Dynamic vehicle route management -- 14.2.1. The vehicle routing problem with time windows -- 14.3. Platform for the solution of the DVRPTW1 -- 14.3.1. Encoding a chromosome -- 14.4. Treating uncertainties in the orders -- 14.5. Treatment of traffic information -- 14.6. Conclusion -- 14.7. Bibliography -- Chapter 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained -- 15.1. Knowledge model -- 15.1.1. Fixed resources and mobile resources -- 15.1.2. Modelling the activities in steps.

15.1.3. The problem to be solved.