Metaheuristics for vehicle routing problems / / Nacima Labadie, Christian Prins, Caroline Prodhon

• Record Nr.: UNINA9910136252503321
• Autore: Labadie Nacima
• Pubbl/distr/stampa: London, England ; ; Hoboken, New Jersey : , : iSTE : , : Wiley, , 2016; ©2016
• ISBN: 1-119-13678-4; 1-119-13676-8; 1-119-13677-6
• Descrizione fisica: 1 online resource (197 p.)
• Collana: Computer Engineering Series. Metaheuristics Set ; ; Volume 3
• Disciplina: 388.310285
• Soggetti: Transportation problems (Programming); Mathematical optimization
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover; Title Page; Copyright; Contents; Notations and Abbreviations; Notations; Abbreviations related to problems; Abbreviations related to methods; Introduction; Chapter 1. General Presentation of Vehicle Routing Problems; 1.1. Logistics management and combinatorial optimization; 1.1.1. History of logistics; 1.1.2. Logistics as a science; 1.1.3. Combinatorial optimization; 1.2. Vehicle routing problems; 1.2.1. Problems in transportation optimization; 1.2.2. Vehicle routing problems in other contexts; 1.2.3. Characteristics of vehicle routing problems; 1.2.3.1. Components; 1.2.3.2. Constraints1.2.3.3. Objectives; 1.2.4. The capacitated vehicle routing problem; 1.2.4.1. Mathematical model; 1.2.4.2. Solution methods; 1.3. Conclusion; Chapter 2. Simple Heuristics and Local Search Procedures; 2.1. Simple heuristics; 2.1.1. Constructive heuristics; 2.1.2. Two-phase methods; 2.1.3. Best-of approach and randomization; 2.2. Local search; 2.2.1. Principle; 2.2.2. Classical moves; 2.2.3. Feasibility tests; 2.2.4. General approach from Vidal et al.; 2.2.5. Multiple neighborhoods; 2.2.6. Very constrained problems; 2.2.7. Acceleration techniques; 2.2.8. Complex moves; 2.3. ConclusionChapter 3. Metaheuristics Generating a Sequence of ; Solutions; 3.1. Simulated annealing (SA); 3.1.1. Principle; 3.1.2. Simulated annealing in vehicle routing problems; 3.2. Greedy randomized adaptive search procedure: GRASP; 3.2.1. Principle; 3.2.2. GRASP in vehicle routing problems; 3.3. Tabu search; 3.3.1. Principle; 3.3.2. Tabu search in vehicle routing problems; 3.4. Variable neighborhood search; 3.4.1. Principle; 3.4.2. Variable neighborhood search in vehicle routing problems; 3.5. Iterated local search; 3.5.1. Principle; 3.5.2. Iterated local search in vehicle routing problems3.6. Guided local search; 3.6.1. Principle; 3.6.2. Guided local search in vehicle routing problems; 3.7. Large neighborhood search; 3.7.1. Principle; 3.7.2. Large neighborhood search in vehicle routing problems; 3.8. Transitional forms; 3.8.1. Evolutionary local search principle; 3.8.2. Application to vehicle routing problems; 3.9. Selected examples; 3.9.1. GRASP for the location-routing problem; 3.9.2. Granular tabu search for the CVRP; 3.9.3. Adaptive large neighborhood search for the pickup and delivery problem with time windows; 3.10. ConclusionChapter 4. Metaheuristics Based on a Set of Solutions; 4.1. Genetic algorithm and its variants; 4.1.1. Genetic algorithm; 4.1.2. Memetic algorithm; 4.1.3. Memetic algorithm with population management; 4.1.4. Genetic algorithm and its variants in vehicle routing problems; 4.2. Scatter search; 4.2.1. Scatter search principle; 4.2.2. Scatter search in vehicle routing problems; 4.3. Path relinking; 4.3.1. Principle; 4.3.2. Path relinking in vehicle routing problems; 4.4. Ant colony optimization; 4.4.1. Principle; 4.4.2. ACO in vehicle routing problems; 4.5. Particle swarm optimization