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Applied Intelligence

, Volume 24, Issue 1, pp 17–30 | Cite as

Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows

  • Beatrice Ombuki
  • Brian J. Ross
  • Franklin Hanshar
Article

Abstract

The Vehicle Routing Problem with Time windows (VRPTW) is an extension of the capacity constrained Vehicle Routing Problem (VRP). The VRPTW is NP-Complete and instances with 100 customers or more are very hard to solve optimally. We represent the VRPTW as a multi-objective problem and present a genetic algorithm solution using the Pareto ranking technique. We use a direct interpretation of the VRPTW as a multi-objective problem, in which the two objective dimensions are number of vehicles and total cost (distance). An advantage of this approach is that it is unnecessary to derive weights for a weighted sum scoring formula. This prevents the introduction of solution bias towards either of the problem dimensions. We argue that the VRPTW is most naturally viewed as a multi-objective problem, in which both vehicles and cost are of equal value, depending on the needs of the user. A result of our research is that the multi-objective optimization genetic algorithm returns a set of solutions that fairly consider both of these dimensions. Our approach is quite effective, as it provides solutions competitive with the best known in the literature, as well as new solutions that are not biased toward the number of vehicles. A set of well-known benchmark data are used to compare the effectiveness of the proposed method for solving the VRPTW.

Keywords

vehicle routing problem with time windows (VRPTW) genetic algorithm multi-objective optimization Pareto ranking 

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Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  • Beatrice Ombuki
    • 1
  • Brian J. Ross
    • 1
  • Franklin Hanshar
    • 1
  1. 1.Department of Computer ScienceBrock UniversitySt. CatharinesCanada

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