Work

Online bipartite matching methodology for anti-epidemic resources allocation: an adaptive time window based on reinforcement learning

Year: 2026

Type: article

Abstract: Background: This study aimed to investigate the online matching problem for anti-epidemic resources among multiple suppliers and recipients in the Internet of Healthcare System during a major outbreak. It accounts for the heterogeneity of supply and demand. Methods: 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's first heuristic strategy to allocate anti-epidemic resources. Results: (ATWBM). However, the ATWBM strategy demonstrates greater effectiveness in adapting to the dynamic changes in epidemic prevention and control, particularly in partially optimistic scenarios. Conclusions: The results revealed 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 changing epidemic dynamics and resource availability.

Source: Frontiers in Public Health

DOI: 10.3389/fpubh.2025.1644499

Authors: Zhiyong Wu, Sulin Pang, Suyan He

Institutions: Guangzhou Huashang College, Guangzhou Huashang Vocational College, Jinan University

Language: English

Topic: Facility Location and Emergency Management

Subfield: Organizational Behavior and Human Resource Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801