ABMS-Driven Reinforcement Learning for Dynamic Resource Allocation in Mass Casualty Incidents †
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| Udgivet i: | Future Internet vol. 17, no. 11 (2025), p. 502-516 |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 100 | 1 | |a Ionuț, Murarețu | |
| 245 | 1 | |a ABMS-Driven Reinforcement Learning for Dynamic Resource Allocation in Mass Casualty Incidents † | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a Romania | |
| 653 | |a Emergency medical care | ||
| 653 | |a Machine learning | ||
| 653 | |a Simulation | ||
| 653 | |a Decision support systems | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Adaptability | ||
| 653 | |a Assignment problem | ||
| 653 | |a Hospitals | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Evacuations & rescues | ||
| 653 | |a Decision making | ||
| 653 | |a Survival | ||
| 653 | |a Optimization | ||
| 653 | |a Resource allocation | ||
| 653 | |a Emergency response | ||
| 653 | |a Linear programming | ||
| 653 | |a Mixed integer | ||
| 653 | |a Mass casualty incidents | ||
| 653 | |a Constraints | ||
| 653 | |a Agent-based models | ||
| 700 | 1 | |a Vultureanu-Albiși Alexandra | |
| 700 | 1 | |a Ilie Sorin | |
| 700 | 1 | |a Costin, Bădică | |
| 773 | 0 | |t Future Internet |g vol. 17, no. 11 (2025), p. 502-516 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275515647/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275515647/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275515647/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |