A simple and effective evolutionary algorithm for the vehicle routing problem

The vehicle routing problem (VRP) plays a central role in the optimization of distribution networks. Since some classical instances with 75 nodes resist the best exact solution methods, most researchers concentrate on metaheuristics for solving real-life problems. Contrary to the VRP with time windo...

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Veröffentlicht in:Computers & operations research Jg. 31; H. 12; S. 1985 - 2002
1. Verfasser: Prins, Christian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.10.2004
Pergamon Press Inc
Elsevier
Schlagworte:
Algorithms
Computer Science
Data Structures and Algorithms
Genetic algorithm
Genetic algorithms
Heuristic
Motor vehicle fleets
Operations Research
Routing
Vehicle routing problem
Genetic algorithm
Vehicle routing problem
ISSN:0305-0548, 1873-765X, 0305-0548
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Zusammenfassung:The vehicle routing problem (VRP) plays a central role in the optimization of distribution networks. Since some classical instances with 75 nodes resist the best exact solution methods, most researchers concentrate on metaheuristics for solving real-life problems. Contrary to the VRP with time windows, no genetic algorithm (GA) can compete with the powerful tabu search (TS) methods designed for the VRP. This paper bridges the gap by presenting a relatively simple but effective hybrid GA. In terms of average solution cost, this algorithm outperforms most published TS heuristics on the 14 classical Christofides instances and becomes the best solution method for the 20 large-scale instances generated by Golden et al. Scope and purpose The framework of this research is the development of effective metaheuristics for hard combinatorial optimization problems met in vehicle routing. It is surprising to notice in the literature the absence of effective genetic algorithms (GA) for the vehicle routing problem (VRP, the main capacitated node routing problem), contrary to node routing problems with time windows or arc routing problems. Earlier attempts were based on chromosomes with trip delimiters and needed a repair procedure to get feasible children after each crossover. Such procedures are known to weaken the genetic transmission of information from parents to children. This paper proposes a GA without trip delimiters, hybridized with a local search procedure. At any time, a chromosome can be converted into an optimal VRP solution (subject to chromosome sequence), thanks to a special splitting procedure. This design choice avoids repair procedures and enables the use of classical crossovers like OX. The resulting algorithm is flexible, relatively simple, and very effective when applied to two sets of standard benchmark instances ranging from 50 to 483 customers.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/S0305-0548(03)00158-8