2026-10-14T16:27:10Z https://www.scidb.cn/oai

10.57760/sciencedb.26176 2025-06-12T17:51:46Z

2025-06-12 Online bipartite matching methodology for resources with major epidemics: adaptive time window based on reinforcement learning doi:10.57760/sciencedb.26176 en This paper studies online matching problem of anti-epidemic resources between demanders and suppliers on the Internet of healthcare systems in a major outbreak, considering the heterogeneity of suppliers and demanders respectively. A time-window-based multi-stage online dynamic bipartite matching model is formulated, which can be transformed into a Markov decision process. The paper proposes adaptive time window batch bipartite matching algorithm based on reinforcement learning with nearest neighbor first heuristic strategy to allocate anti-epidemic resources. The findings indicate that while the matching rate continues to rise, the average waiting time exhibits a tendency of first reducing and then increasing as the matching time window gets longer. This implies that a manager should adjust the matching time window in accordance with the epidemic's development tendency and the availability of resources. It demonstrates that keep exploring the matching time window at a high level with taking the acceptable waiting time into account. Major epidemics; bipartite matching; adaptive time window; resource allocation Zhiyong WU PUBLIC https://www.gnu.org/licenses/gpl-3.0.en.html dataset Science Data Bank