Genetic algorithms for vehicle routing problem with recourse cost model

Jun Qi Chen, Tomohiro Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper deals with the vehicle routing problem involved with System fuzzy vehicle travel times. A two-stage possibility programming model is formulated and the influence of the fuzziness of travel times and service times is treated as recourse cost. By introducing the generalized mean value to define the Fuzzy Mean, the recourse possibility programming model can be transformed into an ordinary programming problem (Slowinski and Hapke in Scheduling under fuzziness. Physics, Heidelberg, 2000) and then a solution method based on Genetic Algorithms is proposed to give the optimal solution of the problem. Finally, some examples are given to illustrate the two-stage model and the solution algorithm.

Original languageEnglish
Title of host publication19th International Conference on Industrial Engineering and Engineering Management
Subtitle of host publicationEngineering Economics Management
Pages903-916
Number of pages14
DOIs
Publication statusPublished - 2013 Dec 1
Event19th International Conference on Industrial Engineering and Engineering Management: Engineering Economics Management - Changsha, China
Duration: 2012 Oct 272012 Oct 29

Publication series

Name19th International Conference on Industrial Engineering and Engineering Management: Engineering Economics Management

Conference

Conference19th International Conference on Industrial Engineering and Engineering Management: Engineering Economics Management
CountryChina
CityChangsha
Period12/10/2712/10/29

Keywords

  • Genetic algorithms
  • Possibility programming
  • Recourse cost
  • Vehicle routing

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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  • Cite this

    Chen, J. Q., & Murata, T. (2013). Genetic algorithms for vehicle routing problem with recourse cost model. In 19th International Conference on Industrial Engineering and Engineering Management: Engineering Economics Management (pp. 903-916). (19th International Conference on Industrial Engineering and Engineering Management: Engineering Economics Management). https://doi.org/10.1007/978-3-642-38442-4_96