Modeling a robust 2-service community healthcare network design problem with central hospital congestion

Abstract

We address a more realistic variant of the community healthcare network design problem, considering a 2-service network framework and hospital congestion structures. Our objective is to minimise the total expected cost through strategic location of central hospitals and allocation of community health stations. Notably, each community health station is allocated to exactly two central hospitals. We also account for uncertainties in resident medical demands, which are challenging to predict accurately due to their volatility. To tackle this, we propose an adaptive distributionally robust optimisation approach that confines uncertain variables to an ambiguity set reflecting demand distribution characteristics. To enhance tractability, we reformulate the distributionally robust model as a mixed-integer linear programme within the specified ambiguity set. Numerical experiments on a real case demonstrate the validity and superiority of the proposed model. Finally, the impact of uncertainty is discussed in depth and some managerial insights for healthcare managers are summarised.

Keywords:

• Community healthcare service network
• hospital congestion
• adaptive optimisation
• distributionally robust optimisation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Human and animal participants This article does not contain any studies with human participants or animals performed by any of the authors.

Data Availability Statement

Data will be made available on request.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant number 61773150 and 71801077; Natural Science Fundation of Hebei Province under Grant number G2022201002 and A2023201020; the Top-notch Talents of Heibei Province under Grant number 702800118009; and the High-Level Innovative Talent Foundation of Hebei University under Grant number 521000981073.