IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v45y2009i6p946-959.html
   My bibliography  Save this article

A multi-population genetic algorithm for transportation scheduling

Author

Listed:
  • Zegordi, S.H.
  • Beheshti Nia, M.A.

Abstract

This study considers the integration of production and transportation scheduling in a two-stage supply chain environment. The objective function minimizes the total tardiness and total deviations of assigned work loads of suppliers from their quotas. After modeling the problem as a mixed integer programming problem, a genetic algorithm with three populations, namely, a multi-society genetic algorithm (MSGA), is proposed for solving it. MSGA is compared with the optimum solutions for small problems and a heuristic and a random search approach for larger problems. Additionally, an MSGA is compared with a generic genetic algorithm. The experimental results show the superiority of the MSGA.

Suggested Citation

  • Zegordi, S.H. & Beheshti Nia, M.A., 2009. "A multi-population genetic algorithm for transportation scheduling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(6), pages 946-959, November.
    • Handle: RePEc:eee:transe:v:45:y:2009:i:6:p:946-959
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554509000726
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ai, Yun-fei & Lu, Jing & Zhang, Li-li, 2015. "The optimization model for the location of maritime emergency supplies reserve bases and the configuration of salvage vessels," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 170-188.
    2. Lihong Pan & Miyuan Shan & Linfeng Li, 2023. "Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
    3. Chen, Gang & Govindan, Kannan & Yang, Zhongzhen, 2013. "Managing truck arrivals with time windows to alleviate gate congestion at container terminals," International Journal of Production Economics, Elsevier, vol. 141(1), pages 179-188.
    4. Yu Zhou & Leishan Zhou & Yun Wang & Zhuo Yang & Jiawei Wu, 2017. "Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem," Complexity, Hindawi, vol. 2017, pages 1-14, July.
    5. Hao Zhang & Yan Cui & Hepu Deng & Shuxian Cui & Huijia Mu, 2021. "An Improved Genetic Algorithm for the Optimal Distribution of Fresh Products under Uncertain Demand," Mathematics, MDPI, vol. 9(18), pages 1-18, September.

    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:eee:transe:v:45:y:2009:i:6:p:946-959. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.