Metaheuristics for logistics. Volume 4

Introduction xi   Part 1. Basic Notions 1    Chapter 1. Introductory Problems 3    1.1. The    swing states    problem 3    1.2. Adel and his camels 5    1.3. Sauron   s forges 7    1.3.1. Problem 1: The inspection of the forges 8    1.3.2. Problem 2: The production of the deadly weapon 9    Chapter 2. A Review of Logistic Problems 13    2.1. Some history 13    2.1.1. The Fermat   Torricelli point 13    2.1.2. The Monge problem 14    2.1.3. The Seven Bridges of Konigsberg and the Icosian Game 15    2.2. Some polynomial problems 16    2.2.1. The assignment problem 16    2.2.2. The transportation problem 17    2.2.3. The Minimum-Cost Spanning Tree problem 19    2.3. Packing problems 20    2.3.1. The knapsack problem 20    2.3.2. The bin packing problem 21    2.4. Routing problems 22    2.4.1. The traveling salesman problem 23    2.4.2. The vehicle routing problem (VRP) 24    2.5. Production scheduling problems 24    2.5.1. The flow-shop scheduling problem (FSSP)26    2.5.2. The job-shop scheduling problem (JSSP) 29    2.6. Lot-sizing problems 31    2.7. Facility location problems 33    2.7.1. The Uncapacitated Plant Location Problem (UPLP) 33    2.7.2. The Dynamic Location Problem (DLP) 35    2.8. Conclusion 36    Chapter 3. An Introduction to Metaheuristics 37    3.1. Optimization problems 37    3.2. Metaheuristics: basic notions 39    3.2.1. Intensification and diversification 40    3.2.2. Neighborhood systems 40    3.3. Individual-based metaheuristics 41    3.3.1. Local search 41    3.3.2. Simulated annealing 44    3.3.3. The kangaroo Algorithm 46    3.3.4. Iterated local search 48    3.3.5. Tabu Search 49    3.4. Population-based metaheuristics 50    3.4.1. Evolutionary algorithms 51    3.4.2. The ant colony algorithm 52    3.4.3. Particle Swarm Optimization  53    3.5. Conclusion 55    Chapter 4. A First Implementation of Metaheuristics 57    4.1. Representing a list of objects 57    4.2. The implementation of a local search 59    4.2.1. The construction of an initial solution 59    4.2.2. Description of basic moves 60    4.2.3. The implementation of stochastic descent (LS) 62    4.3. The implementation of individual-based metaheuristics 64    4.3.1. Simulated annealing (SA) 64    4.3.2. Iterated local search (ILS) 66    4.14. Conclusion 66    Part 2. Advanced Notions 69    Chapter 5. The Traveling Salesman Problem 71    5.1. Representing a solution: the two-level tree structure 71    5.2. Constructing initial solutions 74    5.2.1. A greedy heuristic: nearest neighbor 74    5.2.2. A simplification heuristic: the Christofides algorithm 76    5.3. Neighborhood systems 78    5.3.1. The Lin & Kernighan neighborhood 79    5.3.2. Ejection chain techniques 83    5.4. Some results 86    5.5. Conclusion 88    Chapter 6. The Flow-Shop Problem 89    6.1. Representation and assessment of a solution 89    6.2. Construction of the initial solution 90    6.2.1. Simplification heuristics: CDS 91    6.2.2. A greedy heuristic: NEH 94    6.3. Neighborhood systems 97    6.3.1. Improvement of the insertion movements 98    6.3.2. Variable-depth neighborhood search 101    6.4. Results 107    6.5. Conclusion 107    Chapter 7. Some Elements for Other Logistic Problems 109    7.1. Direct representation versus indirect representation 109    7.2. Conditioning problems 111    7.2.1. The knapsack problem 111    7.2.2. The bin-packing problem 112    7.3. Lot-sizing problems 114    7.4. Localization problems 115    7.5. Conclusion 117    Part 3. Evolutions and Current Trends  119    Chapter 8. Supply Chain Management  121    8.1. Introduction to supply chain management 121    8.2. Horizontal synchronization of the supply chain 122    8.2.1. The beer game 123    8.2.2. The bullwhip effect 125    8.3. Vertical synchronization of a supply chain 126    8.4. An integral approach of the supply chain 127    8.5. Conclusion 129    Chapter 9. Hybridization and Coupling Using Metaheuristics 131    9.1. Metaheuristics for the optimization of the supply chain 131    9.2. Hybridization of optimization methods 133    9.2.1. Classification of hybrid methods 133    9.2.2. Illustration by example 134    9.2.3.    Metaheuristic/local search    hybridization 135    9.2.4. Metaheuristic hybridization/Exact Methods 135    9.3. Coupling of optimization methods and performance evaluations 138    9.3.1. Double complexity 138    9.3.2. Coupling of optimization method/simulation model 139    9.4. Conclusion 141    Chapter 10. Flexible Manufacturing Systems 143    10.1. Introduction to the FMS challenges 143    10.2. The job-shop problem with transport 145    10.2.1. Definition of the problem 145    10.3. Proposal for a metaheuristic/simulation coupling 148    10.3.1. Representation of a solution 148    10.3.2. Simulation method 149    10.3.3. Optimization method 152    10.3.4. Results 153    10.4. Workshop layout problem 154    10.4.1. Aggregated model and exact resolution 154    10.4.2. Detailed model and approximate solutions 157    10.5. Conclusion 159    Chapter 11. Synchronization Problems Based on Vehicle Routings 161    11.1. Inventory routing problem 162    11.1.1. Presentation of the problem 162    11.1.2. Resolution by metaheuristics 166    11.2. The location-routing problem 167    11.2.1. Definition of the problem 167    11.2.2. Solution with metaheuristics 171    11.3. Conclusion 172    Chapter 12. Solution to Problems 173    12.1. The swing state problem 173    12.2. Adel and his camels 176    12.2.1. First question 176    12.2.2. Second question 177    12.2.3. Third question 180    12.3. The forges of Sauron 180    12.3.1. The inspection of the forges 180    12.3.2. Production of the lethal weapon 183    Conclusion 185    Bibliography 187    Index 197