Addressing healthcare operational deficiencies using stochastic and dynamic programming

Na Geng, Xiaolan Xie and Zheng Zhang

International Journal of Production Research, 2019, vol. 57, issue 14, 4371-4390

Abstract: This paper provides an overview of our 10-year research on the application of stochastic and dynamic programming techniques to address health care operational deficiencies in a demand-driven way. We first describe the main operational deficiencies motivating our research in the capacity allocation and scheduling of diagnostic equipment and operating rooms. We then present main findings of extensive field studies to show current practices and key features of the problems under consideration. Applications of stochastic and dynamic programming to these problems are discussed by giving key assumptions, mathematical models, properties of the optimal solution, solution approaches and main numerical findings. The relaxation of the key assumptions is shown to lead to various future research directions that have drawn significant interests of the operations research and industrial engineering communities. We conclude by identifying barriers and potential solutions on the path from theories to applications.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2017.1397789 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:57:y:2019:i:14:p:4371-4390

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2017.1397789

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().