IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v321y2025i1p160-176.html
   My bibliography  Save this article

Optimizing sequential decision-making under risk: Strategic allocation with switching penalties

Author

Listed:
  • Malekipirbazari, Milad

Abstract

This paper considers the multiarmed bandit (MAB) problem augmented with a critical real-world consideration: the cost implications of switching decisions. Our work distinguishes itself by addressing the largely unexplored domain of risk-averse MAB problems compounded by switching penalties. Such scenarios are not just theoretical constructs but are reflective of numerous practical applications. Our contribution is threefold: firstly, we explore how switching costs and risk aversion influence decision-making in MAB problems. Secondly, we present novel theoretical results, including the development of the Risk-Averse Switching Index (RASI), which addresses the dual challenges of risk aversion and switching costs, demonstrating its near-optimal efficacy. This heuristic solution method is grounded in dynamic coherent risk measures, enabling a time-consistent evaluation of risk and reward. Lastly, through rigorous numerical experiments, we validate our algorithm’s effectiveness and practical applicability, providing decision-makers with valuable insights and tools for navigating the multifaceted landscape of risk-averse environments with inherent switching costs.

Suggested Citation

  • Malekipirbazari, Milad, 2025. "Optimizing sequential decision-making under risk: Strategic allocation with switching penalties," European Journal of Operational Research, Elsevier, vol. 321(1), pages 160-176.
    • Handle: RePEc:eee:ejores:v:321:y:2025:i:1:p:160-176
    DOI: 10.1016/j.ejor.2024.09.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221724007264
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2024.09.023?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:eee:ejores:v:321:y:2025:i:1:p:160-176. 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/locate/eor .

    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.