ABMS-Driven Reinforcement Learning for Dynamic Resource Allocation in Mass Casualty Incidents †

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Détails bibliographiques
Publié dans:	Future Internet vol. 17, no. 11 (2025), p. 502-516
Auteur principal:	Ionuț, Murarețu
Autres auteurs:	Vultureanu-Albiși Alexandra, Ilie Sorin, Costin, Bădică
Publié:	MDPI AG
Sujets:	Romania Emergency medical care Machine learning Simulation Decision support systems Artificial intelligence Adaptability Assignment problem Hospitals Optimization techniques Evacuations & rescues Decision making Survival Optimization Resource allocation Emergency response Linear programming Mixed integer Mass casualty incidents Constraints Agent-based models
Accès en ligne:	Citation/Abstract Full Text + Graphics Full Text - PDF
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