IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v337y2024i1d10.1007_s10479-024-05917-6.html
   My bibliography  Save this article

Mathematical programming-based methodology for the evaluation of supply chain collaborative planning scenarios

Author

Listed:
  • D. Pérez-Perales

    (Universitat Politècnica de València (UPV))

  • A. Boza

    (Universitat Politècnica de València (UPV))

  • F. Alarcón

    (Universitat Politècnica de València (UPV))

  • P. Gómez-Gasquet

    (Universitat Politècnica de València (UPV))

Abstract

Nowadays, supply chain (SC) decentralised decision making is the most usual situation in SC operations planning. In this context, different companies can collaboratively plan to achieve a certain level of individual and SC performance. However in many cases, there is reluctance to collaborate because it is not known a priori which benefits will be reported. This paper aims to develop a mathematical programming-based methodology for the evaluation of different supply chain collaborative planning scenarios (MPM-SC-CP). It is assumed that different SC decision centres (DCs) make decisions based on mixed and integer linear programming models. Two main inputs feed the proposed MPM-SC-CP, a framework and associated methodology that support the integrated conceptual and analytical modeling of the SC-CP process in which several DCs make decisions according to spatio-temporal integration. Finally, an application to a real ceramic SC was conducted.

Suggested Citation

  • D. Pérez-Perales & A. Boza & F. Alarcón & P. Gómez-Gasquet, 2024. "Mathematical programming-based methodology for the evaluation of supply chain collaborative planning scenarios," Annals of Operations Research, Springer, vol. 337(1), pages 261-312, June.
    • Handle: RePEc:spr:annopr:v:337:y:2024:i:1:d:10.1007_s10479-024-05917-6
    DOI: 10.1007/s10479-024-05917-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-024-05917-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-024-05917-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    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:spr:annopr:v:337:y:2024:i:1:d:10.1007_s10479-024-05917-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.