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- W4328052143 abstract "Abstract During a disaster the requests for using ambulance services increases. Efficient assignment of the ambulances leads to lowering the patients' travel time. Simulating these environments is very complex and needs a solid framework. This paper uses a Deep Reinforcement Learning approach to better schedule ambulance dispatch problem during those disasters. The concept of a call and assignment of ambulances are illustrated and the elements of states, rewards, and actions in the formulations are described. The algorithm steps for solving this problem are also presented. This paper can help disaster planners to have a better idea for better scheduling ambulances." @default.
- W4328052143 created "2023-03-22" @default.
- W4328052143 creator A5001963769 @default.
- W4328052143 date "2023-03-21" @default.
- W4328052143 modified "2023-10-17" @default.
- W4328052143 title "How hospitals response to disasters; a conceptual deep reinforcement learning approach" @default.
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- W4328052143 doi "https://doi.org/10.21203/rs.3.rs-2715241/v1" @default.
- W4328052143 hasPublicationYear "2023" @default.
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