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|z (OCoLC)939864920
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|a QA402.6
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|a HCDD
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1 |
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|a Labadie, Nacima.
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|a Metaheuristics for vehicle routing problems.
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|a [Place of publication not identified] :
|b John Wiley,
|c 2015.
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| 300 |
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|a 1 online resource
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
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|2 rdacarrier
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|a 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
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|a 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
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|a 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
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|a 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
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|a 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
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|a Transportation problems (Programming)
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|a Mathematical optimization.
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|a Mathematical optimization
|2 fast
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|a Transportation problems (Programming)
|2 fast
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|i has work:
|a Metaheuristics for vehicle routing problems (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGQgD7rtPh3wHktkk3gfC3
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
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|i Print version:
|a Labadie, Nacima.
|t Metaheuristics for vehicle routing problems.
|d [Place of publication not identified] : John Wiley, 2015
|z 1848218117
|z 9781848218116
|w (OCoLC)908914279
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| 856 |
4 |
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|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=4405837
|y Click for online access
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|a EBC-AC
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|a 92
|b HCD
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