{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T17:24:11Z","timestamp":1790011451659,"version":"4.0.1"},"reference-count":35,"publisher":"Institution of Engineering and Technology (IET)","issue":"2","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CAAI Trans on Intel Tech"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>This paper presents HealthNet, a novel framework for the dynamic optimisation of healthcare transportation networks using multi\u2010agent reinforcement learning. HealthNet leverages a spatiotemporal dependency module to capture complex spatiotemporal relationships in healthcare demand and resource allocation patterns, combined with centralised training and a decentralised execution approach. The system is modelled as a Markov game and solved using a deep reinforcement learning algorithm. Extensive simulations demonstrate that HealthNet outperforms eight state\u2010of\u2010the\u2010art baseline methods across multiple network configurations and evaluation metrics. In a 4\u00a0\u00d7\u00a04 grid network, HealthNet reduces average waiting times by 47.6% compared to model predictive control and 22.1% compared to the best\u2010performing baseline. Traffic congestion rates are reduced to 16.7% compared to 42.3% for the worst baseline and 23.1% for the best baseline. Under irregular network topologies with stochastic disruptions, including demand surges and vehicle unavailability, HealthNet maintains superior performance with 42.1% lower average waiting time and 51.1% improvement in peak response times compared to competing approaches. These findings indicate that HealthNet can enhance both efficiency and resilience in healthcare transportation systems, potentially improving patient outcomes in complex urban environments.<\/jats:p>","DOI":"10.1049\/cit2.70125","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:45:21Z","timestamp":1773834321000},"page":"316-331","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi\u2010Agent Reinforcement Learning Driven Dynamic Resource Optimisation in Healthcare Transportation Networks"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6017-8313","authenticated-orcid":false,"given":"Jianhui","family":"Lv","sequence":"first","affiliation":[{"name":"The First Affiliated Hospital of Jinzhou Medical University  Jinzhou 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