A hybrid evolution strategy for the open vehicle routing problem

  • Authors:

  • P. P. Repoussis;C. D. Tarantilis;O. Bräysy;G. Ioannou

  • Affiliations:

  • Department of Management Science and Technology, Athens University of Economics and Business, Patision 76, GR10434 Athens, Greece;Department of Management Science and Technology, Athens University of Economics and Business, Patision 76, GR10434 Athens, Greece;Agora Innoroad Laboratory, Agora Center, University of Jyväskylä, P.O. Box 35, FI-40014, Finland;Department of Management Science and Technology, Athens University of Economics and Business, Patision 76, GR10434 Athens, Greece

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2010

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Abstract

This paper presents a hybrid evolution strategy (ES) for solving the open vehicle routing problem (OVRP), which is a well-known combinatorial optimization problem that addresses the service of a set of customers using a homogeneous fleet of non-depot returning capacitated vehicles. The objective is to minimize the fleet size and the distance traveled. The proposed solution method manipulates a population of @m individuals using a (@m+@l)-ES; at each generation, a new intermediate population of @l offspring is produced via mutation, using arcs extracted from parent individuals. The selection and combination of arcs is dictated by a vector of strategy parameters. A multi-parent recombination operator enables the self-adaptation of the mutation rates based on the frequency of appearance of each arc and the diversity of the population. Finally, each new offspring is further improved via a memory-based trajectory local search algorithm, while an elitist scheme guides the selection of survivors. Experimental results on well-known benchmark data sets demonstrate the competitiveness of the proposed population-based hybrid metaheuristic algorithm.