Outlier-resistant robust medical supply prepositioning and rebalancing in response to disasters

¹ Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing, P.R. China
² Department of Industrial Engineering, Pusan National University, Busan, Republic of Korea
³ School of Traffic and Transportation, Beijing Jiaotong University, Beijing, P.R. China
⁴ School of Economics and Management, Beihang University, Beijing, P.R. China
⁵ Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations, Beijing, P.R. China

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 24 October 2024
Accepted: 10 December 2025

Abstract

The global impact of severe epidemic outbreaks, exemplified by the coronavirus disease 2019 (COVID-19), has been profound. Consequently, epidemic research has received increasing scholarly attention. Effective control of epidemics and saving lives necessitates the implementation of suitable Medical Supply Prepositioning and Rebalancing (MSPR) strategies. These strategies can facilitate rapid responses and address the dissimilarities in infection prevalence across regions, which result in demand-supply mismatches. However, when the input data used for decision-making contains outliers, the optimal solution may deviate significantly from the true optimal solution obtained from outlier-free input data. Thus, the development of robust optimization methods with outlier-resistant characteristics becomes crucial. In this context, this study utilizes two robust estimators, namely weighted Hodges-Lehmann estimators, and formulates three robust nonlinear mathematical models to address the MSPR problem, which aims to mitigate the adverse effects of data contamination. These models are designed to mitigate the adverse effects of outliers on medical supply allocation decisions, enhancing both efficiency and fairness. Then, a comprehensive case study on the threat of COVID-19 in Hubei province during the early stages of the pandemic was conducted to validate the proposed models and methods. The numerical results demonstrate that the newly proposed robust optimization models outperform traditional methods in terms of outlier resistance, solution stability, and allocation efficiency. Ultimately, this study contributes both theoretically and managerially by offering novel modeling techniques and actionable insights for policymakers and practitioners. It underscores the critical importance of integrating robustness into epidemic supply chain planning and provides practical guidance for developing more resilient, reliable, and equitable medical supply strategies under uncertainty.