IDEAS home Printed from https://ideas.repec.org/a/igg/jdsst0/v13y2021i2p1-15.html
   My bibliography  Save this article

Pallet Scheduling Models Under Deterministic and Non-Deterministic Scenarios Using a Hybrid GA Method: Pallet Scheduling Models

Author

Listed:
  • Fuli Zhou

    (Zhengzhou University of Light Industry, China)

  • Yandong He

    (Tsinghua University, China)

Abstract

This study examines the pallet scheduling problem considering random demands under the novel pallet operation mechanism by resources sharing among the pallet sharing system. Two nonlinear integer pallet scheduling models under deterministic and non-deterministic environment are formulated in terms of the pallet demand variable. To solve the pallet programming model, the hybrid genetic algorithm (HGA) integrating local search strategy is designed to derive the optimal pallet scheduling solution. Besides, the fixed sample size sampling strategy is employed to deal with the uncertain demand during the non-deterministic programming model, realized by the Monte Carlo simulation. The two models can assist decision makers arrange a scientific pallet scheduling solution under deterministic and non-deterministic atmosphere. Finally, the numerical case is implemented to testify the effectiveness of the two models and efficiency of the hybrid algorithms.

Suggested Citation

  • Fuli Zhou & Yandong He, 2021. "Pallet Scheduling Models Under Deterministic and Non-Deterministic Scenarios Using a Hybrid GA Method: Pallet Scheduling Models," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 13(2), pages 1-15, April.
    • Handle: RePEc:igg:jdsst0:v:13:y:2021:i:2:p:1-15
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDSST.2021040101
    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:jdsst0:v:13:y:2021:i:2:p:1-15. 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.