IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-1-4614-5885-2_4.html
   My bibliography  Save this book chapter

Optimization in Healthcare Delivery Modeling: Methods and Applications

In: Handbook of Healthcare Operations Management

Author

Listed:
  • Sakine Batun

    (University of Western Ontario)

  • Mehmet A. Begen

    (University of Western Ontario)

Abstract

Optimization methods have been applied to a wide variety of problems in healthcare ranging from operational level scheduling decisions to the design of national healthcare policies. In this chapter, we provide an overview of several practical optimization applications in the domain of healthcare operations management, including appointment scheduling, operating room scheduling, capacity planning, workforce scheduling, healthcare facility location, organ allocation and transplantation, disease screening, and vaccine design. We provide detailed examples to illustrate the use of different optimization techniques such as discrete convex analysis, stochastic programming, and approximate dynamic programming in these areas.

Suggested Citation

  • Sakine Batun & Mehmet A. Begen, 2013. "Optimization in Healthcare Delivery Modeling: Methods and Applications," International Series in Operations Research & Management Science, in: Brian T. Denton (ed.), Handbook of Healthcare Operations Management, edition 127, chapter 0, pages 75-119, Springer.
    • Handle: RePEc:spr:isochp:978-1-4614-5885-2_4
    DOI: 10.1007/978-1-4614-5885-2_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Riise, Atle & Mannino, Carlo & Lamorgese, Leonardo, 2016. "Recursive logic-based Benders’ decomposition for multi-mode outpatient scheduling," European Journal of Operational Research, Elsevier, vol. 255(3), pages 719-728.
    2. Azar, Macarena & Carrasco, Rodrigo A. & Mondschein, Susana, 2022. "Dealing with uncertain surgery times in operating room scheduling," European Journal of Operational Research, Elsevier, vol. 299(1), pages 377-394.
    3. Antoine Legrain & Marie-Andrée Fortin & Nadia Lahrichi & Louis-Martin Rousseau, 2015. "Online stochastic optimization of radiotherapy patient scheduling," Health Care Management Science, Springer, vol. 18(2), pages 110-123, June.

    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:isochp:978-1-4614-5885-2_4. 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.