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• 1. A Brief Primer on Metaheuristics.
• 2. Heuristic Portfolio Selection.
• 3. Risk Budgeted Portfolio Optimization.
• 4. Heuristic Optimization of Equity Market Neutral Portfolios.
• 5. Metaheuristic 130-30 Portfolio Construction.
• 6. Metaheuristic Portfolio Rebalancing with Transaction Costs.
• (source: Nielsen Book Data)

The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.
(source: Nielsen Book Data)

• Preface xi
• Chapter 1 Evolutionary Algorithms 1 1.1 From natural evolution to engineering 1 1.2 A generic evolutionary algorithm 3 1.3 Selection operators 5 1.4 Variation operators and representation 21 1.5 Binary representation 25 1.6 The simple genetic algorithm 30 1.7 Conclusion 31
• Chapter 2 Continuous Optimization 33 2.1 Introduction 33 2.2 Real representation and variation operators for evolutionary algorithms 35 2.3 Covariance Matrix Adaptation Evolution Strategy 46 2.4 A restart CMA Evolution Strategy 55 2.5 Differential Evolution (DE) 57 2.6 Success-History based Adaptive Differential Evolution (SHADE) 65 2.7 Particle Swarm Optimization 70 2.8 Experiments and performance comparisons 77 2.9 Conclusion 88 2.10 Appendix: set of basic objective functions used for the experiments 89
• Chapter 3 Constrained Continuous Evolutionary Optimization 93 3.1 Introduction 93 3.2 Penalization 98 3.3 Superiority of feasible solutions 112 3.4 Evolving on the feasible region 117 3.5 Multi-objective methods 123 3.6 Parallel population approaches 130 3.7 Hybrid methods 132 3.8 Conclusion 132
• Chapter 4 Combinatorial Optimization 135 4.1 Introduction 135 4.2 The binary representation and variation operators 140 4.3 Order-based Representation and variation operators 143 4.4 Conclusion 163
• Chapter 5 Multi-objective Optimization 165 5.1 Introduction 165 5.2 Problem formalization 166 5.3 The quality indicators 167 5.4 Multi-objective evolutionary algorithms 169 5.5 Methods using a "Pareto ranking" 169 5.6 Many-objective problems 176 5.7 Conclusion 181
• Chapter 6 Genetic Programming for Machine Learning 183 6.1 Introduction 183 6.2 Syntax tree representation 186 6.3 Evolving the syntax trees 187 6.4 GP in action: an introductory example 194 6.5 Alternative Genetic Programming Representations 200 6.6 Example of application: intrusion detection in a computer system 210 6.7 Conclusion 215 Bibliography 217 Index 233.
• (source: Nielsen Book Data)

Evolutionary algorithms are bio-inspired algorithms based on Darwin's theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.
(source: Nielsen Book Data)

• Chapter 1. The Science of Biogeography 1 1.1. Introduction 1 1.2. Island biogeography 3 1.3. Influence factors for biogeography 6
• Chapter 2. Biogeography and Biological Optimization 11 2.1. A mathematical model of biogeography 11 2.2. Biogeography as an optimization process 16 2.3. Biological optimization 19 2.3.1. Genetic algorithms 19 2.3.2. Evolution strategies 20 2.3.3. Particle swarm optimization 21 2.3.4. Artificial bee colony algorithm 22 2.4. Conclusion 23
• Chapter 3. A Basic BBO Algorithm 25 3.1. BBO definitions and algorithm 25 3.1.1. Migration 26 3.1.2. Mutation 27 3.1.3. BBO implementation 27 3.2. Differences between BBO and other optimization algorithms 35 3.2.1. BBO and genetic algorithms 35 3.2.2. BBO and other algorithms 36 3.3. Simulations 37 3.4. Conclusion 44
• Chapter 4. BBO Extensions 45 4.1. Migration curves 45 4.2. Blended migration 49 4.3. Other approaches to BBO 51 4.4. Applications 56 4.5. Conclusion 59
• Chapter 5. BBO as a Markov Process 61 5.1. Markov definitions and notations 61 5.2. Markov model of BBO 72 5.3. BBO convergence 79 5.4. Markov models of BBO extensions 90 5.5. Conclusions 99
• Chapter 6. Dynamic System Models of BBO 103 6.1. Basic notation 103 6.2. Dynamic system models of BBO 105 6.3. Applications to benchmark problems 119 6.4. Conclusions 122
• Chapter 7. Statistical Mechanics Approximations of BBO 123 7.1. Preliminary foundation 123 7.2. Statistical mechanics model of BBO 128 7.2.1. Migration 128 7.2.2. Mutation 134 7.3. Further discussion 141 7.3.1. Finite population effects 141 7.3.2. Separable fitness functions 142 7.4. Conclusions 143
• Chapter 8. BBO for Combinatorial Optimization 145 8.1. Traveling salesman problem 147 8.2. BBO for the TSP 148 8.2.1. Population initialization 148 8.2.2. Migration in the TSP 150 8.2.3. Mutation in the TSP 157 8.2.4. Implementation framework 159 8.3. Graph coloring 163 8.4. Knapsack problem 165 8.5. Conclusion 167
• Chapter 9. Constrained BBO 169 9.1. Constrained optimization 170 9.2. Constraint-handling methods 172 9.2.1. Static penalty methods 172 9.2.2. Superiority of feasible points 173 9.2.3. The eclectic evolutionary algorithm 174 9.2.4. Dynamic penalty methods 174 9.2.5. Adaptive penalty methods 176 9.2.6. The niched-penalty approach 177 9.2.7. Stochastic ranking 178 9.2.8. -level comparisons 178 9.3. BBO for constrained optimization 179 9.4. Conclusion 185
• Chapter 10. BBO in Noisy Environments 187 10.1. Noisy fitness functions 188 10.2. Influence of noise on BBO 190 10.3. BBO with re-sampling 193 10.4. The Kalman BBO 196 10.5. Experimental results 199 10.6. Conclusion 201
• Chapter 11. Multi-objective BBO 203 11.1. Multi-objective optimization problems 204 11.2. Multi-objective BBO 211 11.2.1. Vector evaluated BBO 211 11.2.2. Non-dominated sorting BBO 213 11.2.3. Niched Pareto BBO 216 11.2.4. Strength Pareto BBO 218 11.3. Real-world applications 223 11.3.1. Warehouse scheduling model 223 11.3.2. Optimization of warehouse scheduling 229 11.4. Conclusion 231
• Chapter 12. Hybrid BBO Algorithms 233 12.1. Opposition-based BBO 234 12.1.1. Opposition definitions and concepts 234 12.1.2. Oppositional BBO 236 12.1.3. Experimental results 238 12.2. BBO with local search 240 12.2.1. Local search methods 240 12.2.2. Simulation results 245 12.3. BBO with other EAs 247 12.3.1. Iteration-level hybridization 247 12.3.2. Algorithm-level hybridization 250 12.3.3. Experimental results 254 12.4. Conclusion 256 Appendices 259 Appendix A. Unconstrained Benchmark Functions 261 Appendix B. Constrained Benchmark Functions 265 Appendix C. Multi-objective Benchmark Functions 289 Bibliography 309 Index 325.
• (source: Nielsen Book Data)

Evolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This book explains the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.
(source: Nielsen Book Data)

• 1. Single Solution Based Metaheuristics.
• 2. Population-based Methods.
• 3. Performance Evaluation of Metaheuristics.
• 4. Metaheuristics for FACTS Placementâ ¨ and Sizing.
• 5. Genetic Algorithm-based Wind Farm Topology Optimization.
• 6. Topological Study of Electrical Networks.
• 7. Parameter Estimation of α-Stable Distributions.
• 8. SmartGrid and MicroGrid Perspectives.
• (source: Nielsen Book Data)

Intelligence is defined by the ability to optimize, manage and reconcile the currents of physical, economic and even social flows. The strong constraint of immediacy proves to be an opportunity to imagine, propose and deliver solutions on the common basis of optimization techniques. Metaheuristics for Intelligent Electrical Networks analyzes the use of metaheuristics through independent applications but united by the same methodology.
(source: Nielsen Book Data)

• 1. Single Solution Based Metaheuristics.
• 2. Population-based Methods.
• 3. Performance Evaluation of Metaheuristics.
• 4. Metaheuristics for FACTS Placementâ ¨ and Sizing.
• 5. Genetic Algorithm-based Wind Farm Topology Optimization.
• 6. Topological Study of Electrical Networks.
• 7. Parameter Estimation of α-Stable Distributions.
• 8. SmartGrid and MicroGrid Perspectives.
• (source: Nielsen Book Data)

Intelligence is defined by the ability to optimize, manage and reconcile the currents of physical, economic and even social flows. The strong constraint of immediacy proves to be an opportunity to imagine, propose and deliver solutions on the common basis of optimization techniques. Metaheuristics for Intelligent Electrical Networks analyzes the use of metaheuristics through independent applications but united by the same methodology.
(source: Nielsen Book Data)

• 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 Koenigsberg 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.
• (source: Nielsen Book Data)

This book describes the main classical combinatorial problems that can be encountered when designing a logistics network or driving a supply chain. It shows how these problems can be tackled by metaheuristics, both separately and using an integrated approach. A huge number of techniques, from the simplest to the most advanced ones, are given for helping the reader to implement efficient solutions that meet its needs. A lot of books have been written about metaheuristics (methods for solving hard optimization problems) and supply chain management (the field in which we find a huge number of combinatorial optimization problems) in the last decades. So, the main reason of this book is to describe how these methods can be implemented for this class of problems.
(source: Nielsen Book Data)

• 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 Koenigsberg 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.
• (source: Nielsen Book Data)

This book describes the main classical combinatorial problems that can be encountered when designing a logistics network or driving a supply chain. It shows how these problems can be tackled by metaheuristics, both separately and using an integrated approach. A huge number of techniques, from the simplest to the most advanced ones, are given for helping the reader to implement efficient solutions that meet its needs. A lot of books have been written about metaheuristics (methods for solving hard optimization problems) and supply chain management (the field in which we find a huge number of combinatorial optimization problems) in the last decades. So, the main reason of this book is to describe how these methods can be implemented for this class of problems.
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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..
(source: Nielsen Book Data)