A healthcare facility location problem for a multi -disease, multi -service environment under risk aversion

Date

2020-09-01

Author

Taymaz, S.
Iyigun, C.
Bayındır, Zeynep Pelin
Dellaert, N. P.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper presents a stochastic optimisation model for locating walk-in clinics for mobile populations in a network. The walk-in clinics ensure a continuum of care for the mobile population across the network by offering a perpetuation of services along the transportation lines, and also establishing referral systems to local healthcare facilities. The continuum of care requirements for different diseases is modelled using coverage definitions that are designed specifically to reflect the adherence protocols for services for different diseases. The risk of not providing the required care under different realisations of health service demand is considered. In this paper, for a multi-disease, multi-service environment, we propose a model to determine the location of roadside walk-in clinics and their assigned services. The objective is to maximise the total expected weighted coverage of the network subject to a Conditional-Value-at-Risk (CVaR) measure. This paper presents developed coverage definitions, the optimisation model and the computational study carried out on a real-life case in Africa.

Subject Keywords

Management Science and Operations Research, Strategy and Management, Statistics, Probability and Uncertainty, Geography, Planning and Development, Economics and Econometrics

URI

https://hdl.handle.net/11511/42634

Journal

SOCIO-ECONOMIC PLANNING SCIENCES

DOI

https://doi.org/10.1016/j.seps.2019.100755

Collections

Department of Industrial Engineering, Article

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Citation Formats

S. Taymaz, C. Iyigun, Z. P. Bayındır, and N. P. Dellaert, “A healthcare facility location problem for a multi -disease, multi -service environment under risk aversion,” SOCIO-ECONOMIC PLANNING SCIENCES, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42634.