Metaheuristics for Vehicle Routing Problems
1st Edition
Published on 12. February 2016
Book
Hardback
194 pages
978-1-84821-811-6 (ISBN)
Description
This book is dedicated to metaheuristics as applied to vehicle routing problems. Several implementations are given as illustrative examples, along with applications to several typical vehicle routing problems.
As a first step, a general presentation intends to make the reader more familiar with the related field of logistics and combinatorial optimization. This preamble is completed with a description of significant heuristic methods classically used to provide feasible solutions quickly, and local improvement moves widely used to search for enhanced solutions. The overview of these fundamentals allows appreciating the core of the work devoted to an analysis of metaheuristic methods for vehicle routing problems. Those methods are exposed according to their feature of working either on a sequence of single solutions, or on a set of solutions, or even by hybridizing metaheuristic approaches with others kind of methods.
As a first step, a general presentation intends to make the reader more familiar with the related field of logistics and combinatorial optimization. This preamble is completed with a description of significant heuristic methods classically used to provide feasible solutions quickly, and local improvement moves widely used to search for enhanced solutions. The overview of these fundamentals allows appreciating the core of the work devoted to an analysis of metaheuristic methods for vehicle routing problems. Those methods are exposed according to their feature of working either on a sequence of single solutions, or on a set of solutions, or even by hybridizing metaheuristic approaches with others kind of methods.
More details
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 15 mm
Weight
463 gr
ISBN-13
978-1-84821-811-6 (9781848218116)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Nacima Labadie | Christian Prins | Caroline Prodhon
Metaheuristics for Vehicle Routing Problems
E-Book
02/2016
Wiley-ISTE
€139.99
Available for download
Nacima Labadie | Christian Prins | Caroline Prodhon
Metaheuristics for Vehicle Routing Problems
E-Book
02/2016
Wiley-ISTE
€139.99
Available for download
Persons
Nacima Labadie is Associate Professor with accreditation to supervise research (HDR) at the Industrial Systems Optimization Laboratory of the Charles Delaunay Institute at the University of Technology of Troyes in France. Her research mainly focuses on arc routing and team orienteering problems.
Christian Prins is Full Professor at the Industrial Systems Optimization Laboratory of the Charles Delaunay Institute at the University of Technology of Troyes in France. His research focuses on the many variants of vehicle routing problems.
Caroline Prodhon is Associate Professor with accreditation to supervise research (HDR) at the Industrial Systems Optimization Laboratory of the Charles Delaunay Institute at the University of Technology of Troyes in France. Her research focuses on location-routing problems.
Christian Prins is Full Professor at the Industrial Systems Optimization Laboratory of the Charles Delaunay Institute at the University of Technology of Troyes in France. His research focuses on the many variants of vehicle routing problems.
Caroline Prodhon is Associate Professor with accreditation to supervise research (HDR) at the Industrial Systems Optimization Laboratory of the Charles Delaunay Institute at the University of Technology of Troyes in France. Her research focuses on location-routing problems.
Content
Notations and Abbreviations ix Introduction xiii
Chapter 1. General Presentation of Vehicle Routing Problems 1
1.1. Logistics management and combinatorial optimization 1
1.1.1. History of logistics 2
1.1.2. Logistics as a science 5
1.1.3. Combinatorial optimization 5
1.2. Vehicle routing problems 6
1.2.1. Problems in transportation optimization 6
1.2.2. Vehicle routing problems in other contexts 7
1.2.3. Characteristics of vehicle routing problems 7
1.2.4. The capacitated vehicle routing problem 11
1.3. Conclusion 13
Chapter 2. Simple Heuristics and Local Search Procedures 15
2.1. Simple heuristics 16
2.1.1. Constructive heuristics 16
2.1.2. Two-phase methods 19
2.1.3. Best-of approach and randomization 22
2.2. Local search 23
2.2.1. Principle 23
2.2.2. Classical moves 24
2.2.3. Feasibility tests 25
2.2.4. General approach from Vidal et al 28
2.2.5. Multiple neighborhoods 30
2.2.6. Very constrained problems 33
2.2.7. Acceleration techniques 33
2.2.8. Complex moves 36
2.3. Conclusion 37
Chapter 3. Metaheuristics Generating a Sequence of Solutions 39
3.1. Simulated annealing (SA) 39
3.1.1. Principle 39
3.1.2. Simulated annealing in vehicle routing problems 40
3.2. Greedy randomized adaptive search procedure: GRASP 41
3.2.1. Principle 41
3.2.2. GRASP in vehicle routing problems 43
3.3. Tabu search 44
3.3.1. Principle 44
3.3.2. Tabu search in vehicle routing problems 45
3.4. Variable neighborhood search 47
3.4.1. Principle 47
3.4.2. Variable neighborhood search in vehicle routing problems 49
3.5. Iterated local search 50
3.5.1. Principle 50
3.5.2. Iterated local search in vehicle routing problems 52
3.6. Guided local search 54
3.6.1. Principle 54
3.6.2. Guided local search in vehicle routing problems 55
3.7. Large neighborhood search 56
3.7.1. Principle 56
3.7.2. Large neighborhood search in vehicle routing problems 58
3.8. Transitional forms 59
3.8.1. Evolutionary local search principle 59
3.8.2. Application to vehicle routing problems 60
3.9. Selected examples 61
3.9.1. GRASP for the location-routing problem 61
3.9.2. Granular tabu search for the CVRP 65
3.9.3. Adaptive large neighborhood search for the pickup and delivery problem with time windows 69
3.10. Conclusion 74
Chapter 4. Metaheuristics Based on a Set of Solutions 77
4.1. Genetic algorithm and its variants 77
4.1.1. Genetic algorithm 77
4.1.2. Memetic algorithm 79
4.1.3. Memetic algorithm with population management 79
4.1.4. Genetic algorithm and its variants in vehicle routing problems 80
4.2. Scatter search 82
4.2.1. Scatter search principle 82
4.2.2. Scatter search in vehicle routing problems 83
4.3. Path relinking 83
4.3.1. Principle 84
4.3.2. Path relinking in vehicle routing problems 85
4.4. Ant colony optimization 86
4.4.1. Principle 86
4.4.2. ACO in vehicle routing problems 89
4.5. Particle swarm optimization 89
4.5.1. Principle 89
4.5.2. PSO in vehicle routing problems 90
4.6. Other approaches and their use in vehicle routing problems 91
4.7. Selected examples 92
4.7.1. Scatter search for the periodic capacitated arc routing problem 92
4.7.2. PR for the muti-depot periodic VRP 97
4.7.3. Unified genetic algorithm for a wide class of vehicle routing problems 101
4.8. Conclusion 106
Chapter 5. Metaheuristics Hybridizing Various Components 109
5.1. Hybridizing metaheuristics 109
5.1.1. Principle 110
5.1.2. Application to vehicle routing problems 111
5.1.3. Selected examples 112
5.2. Matheuristics 122
5.2.1. Principle 123
5.2.2. Application to vehicle routing problems 124
5.2.3. Selected examples 128
5.3. Conclusion 144
Conclusion 145
Bibliography 149
Index 167
Chapter 1. General Presentation of Vehicle Routing Problems 1
1.1. Logistics management and combinatorial optimization 1
1.1.1. History of logistics 2
1.1.2. Logistics as a science 5
1.1.3. Combinatorial optimization 5
1.2. Vehicle routing problems 6
1.2.1. Problems in transportation optimization 6
1.2.2. Vehicle routing problems in other contexts 7
1.2.3. Characteristics of vehicle routing problems 7
1.2.4. The capacitated vehicle routing problem 11
1.3. Conclusion 13
Chapter 2. Simple Heuristics and Local Search Procedures 15
2.1. Simple heuristics 16
2.1.1. Constructive heuristics 16
2.1.2. Two-phase methods 19
2.1.3. Best-of approach and randomization 22
2.2. Local search 23
2.2.1. Principle 23
2.2.2. Classical moves 24
2.2.3. Feasibility tests 25
2.2.4. General approach from Vidal et al 28
2.2.5. Multiple neighborhoods 30
2.2.6. Very constrained problems 33
2.2.7. Acceleration techniques 33
2.2.8. Complex moves 36
2.3. Conclusion 37
Chapter 3. Metaheuristics Generating a Sequence of Solutions 39
3.1. Simulated annealing (SA) 39
3.1.1. Principle 39
3.1.2. Simulated annealing in vehicle routing problems 40
3.2. Greedy randomized adaptive search procedure: GRASP 41
3.2.1. Principle 41
3.2.2. GRASP in vehicle routing problems 43
3.3. Tabu search 44
3.3.1. Principle 44
3.3.2. Tabu search in vehicle routing problems 45
3.4. Variable neighborhood search 47
3.4.1. Principle 47
3.4.2. Variable neighborhood search in vehicle routing problems 49
3.5. Iterated local search 50
3.5.1. Principle 50
3.5.2. Iterated local search in vehicle routing problems 52
3.6. Guided local search 54
3.6.1. Principle 54
3.6.2. Guided local search in vehicle routing problems 55
3.7. Large neighborhood search 56
3.7.1. Principle 56
3.7.2. Large neighborhood search in vehicle routing problems 58
3.8. Transitional forms 59
3.8.1. Evolutionary local search principle 59
3.8.2. Application to vehicle routing problems 60
3.9. Selected examples 61
3.9.1. GRASP for the location-routing problem 61
3.9.2. Granular tabu search for the CVRP 65
3.9.3. Adaptive large neighborhood search for the pickup and delivery problem with time windows 69
3.10. Conclusion 74
Chapter 4. Metaheuristics Based on a Set of Solutions 77
4.1. Genetic algorithm and its variants 77
4.1.1. Genetic algorithm 77
4.1.2. Memetic algorithm 79
4.1.3. Memetic algorithm with population management 79
4.1.4. Genetic algorithm and its variants in vehicle routing problems 80
4.2. Scatter search 82
4.2.1. Scatter search principle 82
4.2.2. Scatter search in vehicle routing problems 83
4.3. Path relinking 83
4.3.1. Principle 84
4.3.2. Path relinking in vehicle routing problems 85
4.4. Ant colony optimization 86
4.4.1. Principle 86
4.4.2. ACO in vehicle routing problems 89
4.5. Particle swarm optimization 89
4.5.1. Principle 89
4.5.2. PSO in vehicle routing problems 90
4.6. Other approaches and their use in vehicle routing problems 91
4.7. Selected examples 92
4.7.1. Scatter search for the periodic capacitated arc routing problem 92
4.7.2. PR for the muti-depot periodic VRP 97
4.7.3. Unified genetic algorithm for a wide class of vehicle routing problems 101
4.8. Conclusion 106
Chapter 5. Metaheuristics Hybridizing Various Components 109
5.1. Hybridizing metaheuristics 109
5.1.1. Principle 110
5.1.2. Application to vehicle routing problems 111
5.1.3. Selected examples 112
5.2. Matheuristics 122
5.2.1. Principle 123
5.2.2. Application to vehicle routing problems 124
5.2.3. Selected examples 128
5.3. Conclusion 144
Conclusion 145
Bibliography 149
Index 167