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Solving a Bi-objective Vehicle Routing Problem by Pareto-Ant Colony Optimization

  • Joseph M. Pasia
  • Karl F. Doerner
  • Richard F. Hartl
  • Marc Reimann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4638)

Abstract

In this paper we propose the application of Pareto ant colony optimization (PACO) in solving a bi-objective capacitated vehicle routing problem with route balancing (CVRPRB). The objectives of the problem are minimization of the tour length and balancing the routes. We propose PACO as our response to the deficiency of the Pareto-based local search (P-LS) approach, which we also developed to solve CVRPRB. The deficiency of P-LS is the lack of information flow among its pools of solutions. PACO is a natural choice in addressing this deficiency since PACO and P-LS are similar in structure. It resolves the absence of information flow through its pheromone values. Several test instances are used to demonstrate the contribution and importance of information flow among the pools of solutions. Computational results show that PACO improves P-LS in most instances with respect to different performance metrics.

Keywords

Local Search Test Instance Tour Length Capacitate Vehicle Rout Problem Local Search Phase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Joseph M. Pasia
    • 1
  • Karl F. Doerner
    • 2
    • 3
  • Richard F. Hartl
    • 2
  • Marc Reimann
    • 4
  1. 1.Department of Mathematics, University of the Philippines-Diliman, Quezon CityPhilippines
  2. 2.Department of Business Administration, University of Vienna, ViennaAustria
  3. 3.Salzburg Research Forschungsgesellschaft, SalzburgAustria
  4. 4.Institute for Operations Research, ETH Zurich, ZurichSwitzerland

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