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

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

The optimal window size in fixed time window batch matching strategy (FTWBM) outperforms the results of adaptive time window batch matching strat

SUBMITTER: Wu Z 

PROVIDER: S-EPMC12823865 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature