An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

  • Authors:

  • Jari Kytöjoki;Teemu Nuortio;Olli Bräysy;Michel Gendreau

  • Affiliations:

  • Agora Innoroad Laboratory, Agora Center, University of Jyväskylä, P.O. Box 35, FI-40014, Finland;Department of Environmental Sciences, University of Kuopio, P.O. Box 1627, FI-70211 Kuopio, Finland;Agora Innoroad Laboratory, Agora Center, University of Jyväskylä, P.O. Box 35, FI-40014, Finland;Center for Research on Transportation, University of Montreal, P.O.Box 6128, Succursale Centre-ville, Montreal, H3C 3J7, Canada

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2007

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Abstract

In this paper, we present an efficient variable neighborhood search heuristic for the capacitated vehicle routing problem. The objective is to design least cost routes for a fleet of identically capacitated vehicles to service geographically scattered customers with known demands. The variable neighborhood search procedure is used to guide a set of standard improvement heuristics. In addition, a strategy reminiscent of the guided local search metaheuristic is used to help escape local minima. The developed solution method is specifically aimed at solving very large scale real-life vehicle routing problems. To speed up the method and cut down memory usage, new implementation concepts are used. Computational experiments on 32 existing large scale benchmarks, as well as on 20 new very large scale problem instances, demonstrate that the proposed method is fast, competitive and able to find high-quality solutions for problem instances with up to 20,000 customers within reasonable CPU times.