ISBN-10:
1848218087
ISBN-13:
9781848218086
Pub. Date:
02/23/2016
Publisher:
Wiley
Metaheuristics for Logistics / Edition 1

Metaheuristics for Logistics / Edition 1

by Laurent Deroussi

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Product Details

ISBN-13: 9781848218086
Publisher: Wiley
Publication date: 02/23/2016
Pages: 222
Product dimensions: 6.20(w) x 9.30(h) x 0.80(d)

About the Author

Laurent Deroussi, Associate professor at the Blaise Pascal University of Clermont-Ferrand, France.

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Table of Contents

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 Königsberg 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

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