ORIGINAL RESEARCH article
Front. Public Health
Sec. Disaster and Emergency Medicine
Online bipartite matching methodology for anti-epidemic resources allocation: an adaptive time window based on reinforcement learning
Provisionally accepted
- 1Guangzhou Huashang College, Guangzhou, China
- 2Jinan University, Guangzhou, China
- 3Communist Party of China Guangdong Province Committee Party School, Guangzhou, China
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This study investigates the online matching problem for anti-epidemic resources among multiple suppliers and recipients in the Internet of Healthcare System during a major outbreak. It takes into account the heterogeneity of supply and demand. A multi-stage online dynamic bipartite matching model based on time windows is developed, which can be reformulated as a Markov decision process. An adaptive time window batch bipartite matching algorithm based on reinforcement learning is proposed, which utilizes the nearest neighbor first heuristic strategy to allocate anti-epidemic resources. The results reveal that, although the average matching rate consistently increases, the average waiting time initially decreases before rising again as the matching time window expands. This finding implies that health operations managers should modify the matching time window in response to the changing dynamics of the epidemic and resource availability. This study highlights the importance of continually exploring the optimal matching time window at a higher level while also considering acceptable waiting times.
Keywords: major epidemics, Bipartite matching, Adaptive time window, Resource Allocation, reinforcement learning
Received: 16 Jun 2025; Accepted: 02 Dec 2025.
Copyright: © 2025 Wu, Pang and He. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhiyong Wu
Sulin Pang
Suyan He
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