Active guided evolution strategies for large-scale vehicle routing problems with time windows

  • Authors:

  • David Mester;Olli Bräysy

  • Affiliations:

  • Institute of Ecolution, Mathematical and Population Genetics Laboratory, University of Haifa, Israel;SINTEF Applied Mathematics, Department of Optimization, Blindern, Oslo, Norway

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2005

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Abstract

We present a new and effective metaheuristic algorithm, active guided evolution strategies, for the vehicle routing problem with time windows. The algorithm combines the strengths of the well-known guided local search and evolution strategies metaheuristics into an iterative two-stage procedure. More precisely, guided local search is used to regulate a composite local search in the first stage and the neighborhood of the evolution strategies algorithm in the second stage. The vehicle routing problem with time windows is a classical problem in operations research, where the objective is to design least cost routes for a fleet of identical capacitated vehicles to service geographically scattered customers within pre-specified time windows. The presented algorithm is specifically designed for large-scale problems. The computational experiments were carried out on an extended set of 302 benchmark problems. The results demonstrate that the suggested method is highly competitive, providing the best-known solutions to 86% of all test instances within reasonable computing times. The power of the algorithm is confirmed by the results obtained on 23 capacitated vehicle routing problems from the literature.