150. A Markov Chain-Based Relocation and Districting Model for Optimizing Emergency Medical Services
Invited abstract in session TC-11: Emergency care and services, stream OR in Healthcare (ORAHS).
Tuesday, 12:30-14:00Room: Clarendon SR 1.03
Authors (first author is the speaker)
| 1. | Lei Liu
|
| Management Science and Analytics, University of Nottingham Ningbo China(UNNC) | |
| 2. | Yiwen Hu
|
| University of Nottingham Ningbo China |
Abstract
Ambulance relocation is a critical strategy in Emergency Medical Service (EMS) systems, aimed at repositioning idle ambulances to enhance coverage in high-priority areas. Compliance table policies are commonly used in EMS systems to optimize ambulance deployment. To further enhance the efficiency of EMS call responses across large geographic regions, we propose a novel relocation model that combines a Markov chain framework with sub-region-based repositioning. This approach enables the EMS system to allocate resources more effectively to high-priority areas while reducing the time spent on ambulance relocations. The model's effectiveness and practical applicability are demonstrated through numerical results derived from real-world EMS operations in a Chinese city with a population of over 9 million.
Keywords
- Health Care
- OR/MS and the Public Sector
- Stochastic Models
Status: accepted
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