Conference Proceeding Article
In emergency medical systems, arriving at the incident location a few seconds early can save a human life. Thus, this paper is motivated by the need to reduce the response time – time taken to arrive at the incident location after receiving the emergency call – of Emergency Response Vehicles, ERVs (ex: ambulances, fire rescue vehicles) for as many requests as possible. We expect to achieve this primarily by positioning the "right" number of ERVs at the "right" places and at the "right" times. Given the exponentially large action space (with respect to number of ERVs and their placement) and the stochasticity in location and timing of emergency incidents, this problem is computationally challenging. To that end, our contributions building on existing data-driven approaches are three fold. Finally, we provide an exhaustive evaluation on real-world datasets from two asian cities that demonstrates the improvement provided by our approach over current practice and the best known approach from literature.
Computer Sciences | Health and Medical Administration | Operations Research, Systems Engineering and Industrial Engineering
Intelligent Systems and Decision Analytics
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence: 25-30 January 2015, Austin, Texas
City or Country
Palo Alto, CA
SAISUBRAMANIAN, Sandhya; Pradeep VARAKANTHAM; and LAU, Hoong Chuin.
Risk based Optimization for Improving Emergency Medical Systems. (2015). Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence: 25-30 January 2015, Austin, Texas. 702-708. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2850
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