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A Population-Based Local Search for Solving a Bi-objective Vehicle Routing Problem

  • Joseph M. Pasia
  • Karl F. Doerner
  • Richard F. Hartl
  • Marc Reimann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4446)

Abstract

In this paper we present a population-based local search for solving a bi-objective vehicle routing problem. The objectives of the problem are minimization of the tour length and balancing the routes. The algorithm repeatedly generates a pool of good initial solutions by using a randomized savings algorithm followed by local search. The local search uses three neighborhood structures and evaluates the fitness of candidate solutions using dominance relation. Several test instances are used to assess the performance of the new approach. Computational results show that the population-based local search outperforms the best known algorithm for this problem.

Keywords

Local Search Pareto Front Test Instance Vehicle Rout Problem Capacitate Vehicle Rout Problem 
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 Berlin Heidelberg 2007

Authors and Affiliations

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

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