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

Ionuț Murarețu (), Alexandra Vultureanu-Albiși, Sorin Ilie and Costin Bădică
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Ionuț Murarețu: Department of Computers and Information Technology, University of Craiova, Strada Alexandru Ioan Cuza 13, 200585 Craiova, Romania
Alexandra Vultureanu-Albiși: Department of Computers and Information Technology, University of Craiova, Strada Alexandru Ioan Cuza 13, 200585 Craiova, Romania
Sorin Ilie: Department of Computers and Information Technology, University of Craiova, Strada Alexandru Ioan Cuza 13, 200585 Craiova, Romania
Costin Bădică: Department of Computers and Information Technology, University of Craiova, Strada Alexandru Ioan Cuza 13, 200585 Craiova, Romania

Future Internet, 2025, vol. 17, issue 11, 1-14

Abstract: This paper introduces a novel framework that integrates reinforcement learning with declarative modeling and mathematical optimization for dynamic resource allocation during mass casualty incidents. Our approach leverages Mesa as an agent-based modeling library to develop a flexible and scalable simulation environment as a decision support system for emergency response. This paper addresses the challenge of efficiently allocating casualties to hospitals by combining mixed-integer linear and constraint programming while enabling a central decision-making component to adapt allocation strategies based on experience. The two-layer architecture ensures that casualty-to-hospital assignments satisfy geographical and medical constraints while optimizing resource usage. The reinforcement learning component receives feedback through agent-based simulation outcomes, using survival rates as the reward signal to guide future allocation decisions. Our experimental evaluation, using simulated emergency scenarios, shows a significant improvement in survival rates compared to traditional optimization approaches. The results indicate that the hybrid approach successfully combines the robustness of declarative modeling and the adaptability required for smart decision making in complex and dynamic emergency scenarios.

Keywords: mass casualty incidents; dynamic optimization; reinforcement learning; declarative modeling (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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