Robust optimization model for medical staff rebalancing problem with data contamination during COVID-19 pandemic

Xuehong Gao, Guozhong Huang, Qiuhong Zhao, Cejun Cao and Huiling Jiang

International Journal of Production Research, 2022, vol. 60, issue 5, 1737-1766

Abstract: After the outbreak of the COVID-19 pandemic, the naturally dissimilar prevalence of infection resulted in a growing imbalance between supply and demand for medical staff. Rebalancing the medical staff seems a pressing task following the uncertain environment. However, once the collected data are contaminated, the optimal solution obtained through traditional methods may be located far away from the true one. In this sense, finding a robust optimization method that is less sensitive to outliers and accounts for uncertain future events is warranted. Consequently, this study deeply investigates the medical staff rebalancing problem with data contamination and proposes two robust optimization models to cure the detrimental consequences caused by contaminated data. Due to the nonlinearity of the proposed robust models, the corresponding linearisation approaches are developed to determine the unique medical staff rebalancing scheme. To validate the proposed models and methods, a real case study from the U.S. is implemented. Finally, study results indicate that the proposed methods can overcome the effects of data contamination, and deep managerial implications and actionable insights from theory and practice regarding the cooperation mechanism and medical staff rebalancing strategies are drawn from the case study, which provides the main needs and benefits of this study.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2021.1995793 (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:60:y:2022:i:5:p:1737-1766

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

DOI: 10.1080/00207543.2021.1995793

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 ().