IDEAS home Printed from https://ideas.repec.org/a/igg/jban00/v6y2019i1p55-76.html
   My bibliography  Save this article

A Modified Kruskal's Algorithm to Improve Genetic Search for Open Vehicle Routing Problem

Author

Listed:
  • Joydeep Dutta

    (National Institute of Technology Durgapur, West Bengal, India)

  • Partha Sarathi Barma

    (National Institute of Technology Durgapur, West Bengal, India)

  • Samarjit Kar

    (National Institute of Technology Durgapur, West Bengal, India)

  • Tanmay De

    (National Institute of Technology Durgapur, West Bengal, India)

Abstract

This article has proposed a modified Kruskal's method to increase the efficiency of a genetic algorithm to determine the path of least distance starting from a central point to solve the open vehicle routing problem. In a vehicle routing problem, vehicles start from a central point and several customers placed in different locations to serve their demands and return to the central point. In the case of the open vehicle routing problem, the vehicles do not go back to the central point after serving the customers. The challenge is to reduce the number of vehicles used and the distance travelled simultaneously. The proposed method applies genetic algorithms to find the set of customers those are covered by a particular vehicle and the authors have applied the proposed modified Kruskal's method for local routing optimization. The results of the new method are analyzed in comparison with some of the evolutionary methods.

Suggested Citation

  • Joydeep Dutta & Partha Sarathi Barma & Samarjit Kar & Tanmay De, 2019. "A Modified Kruskal's Algorithm to Improve Genetic Search for Open Vehicle Routing Problem," International Journal of Business Analytics (IJBAN), IGI Global, vol. 6(1), pages 55-76, January.
    • Handle: RePEc:igg:jban00:v:6:y:2019:i:1:p:55-76
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.2019010104
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jban00:v:6:y:2019:i:1:p:55-76. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.