Lam Sean Shao Wei, Ng Clarence Boon Liang, Nguyen Francis Ngoc Hoang Long, Ng Yih Yng, Ong Marcus Eng Hock
Health Services Research Centre, Singapore Health Services; Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore.
Department of Industrial and Systems Engineering, National University of Singapore, Singapore.
Int J Med Inform. 2017 Oct;106:37-47. doi: 10.1016/j.ijmedinf.2017.06.005. Epub 2017 Jun 30.
Dynamic ambulance redeployment policies tend to introduce much more flexibilities in improving ambulance resource allocation by capitalizing on the definite geospatial-temporal variations in ambulance demand patterns over the time-of-the-day and day-of-the-week effects. A novel modelling framework based on the Approximate Dynamic Programming (ADP) approach leveraging on a Discrete Events Simulation (DES) model for dynamic ambulance redeployment in Singapore is proposed in this paper.
The study was based on the Singapore's national Emergency Medical Services (EMS) system. Based on a dataset comprising 216,973 valid incidents over a continuous two-years study period from 1 January 2011-31 December 2012, a DES model for the EMS system was developed. An ADP model based on linear value function approximations was then evaluated using the DES model via the temporal difference (TD) learning family of algorithms. The objective of the ADP model is to derive approximate optimal dynamic redeployment policies based on the primary outcome of ambulance coverage.
Considering an 8min response time threshold, an estimated 5% reduction in the proportion of calls that cannot be reached within the threshold (equivalent to approximately 8000 dispatches) was observed from the computational experiments. The study also revealed that the redeployment policies which are restricted within the same operational division could potentially result in a more promising response time performance. Furthermore, the best policy involved the combination of redeploying ambulances whenever they are released from service and that of relocating ambulances that are idle in bases.
This study demonstrated the successful application of an approximate modelling framework based on ADP that leverages upon a detailed DES model of the Singapore's EMS system to generate approximate optimal dynamic redeployment plans. Various policies and scenarios relevant to the Singapore EMS system were evaluated.
动态救护车重新部署政策倾向于通过利用一天中不同时段和一周中不同日期救护车需求模式明确的地理时空变化，在改善救护车资源分配方面引入更多灵活性。本文提出了一种基于近似动态规划（ADP）方法的新型建模框架，该框架利用离散事件模拟（DES）模型对新加坡的救护车进行动态重新部署。
该研究基于新加坡的国家紧急医疗服务（EMS）系统。基于2011年1月1日至2012年12月31日连续两年研究期间的216973起有效事件数据集，开发了EMS系统的DES模型。然后，通过时间差分（TD）学习算法家族，使用DES模型对基于线性值函数近似的ADP模型进行评估。ADP模型的目标是根据救护车覆盖范围的主要结果得出近似最优的动态重新部署政策。
考虑到8分钟的响应时间阈值，计算实验观察到在该阈值内无法响应的呼叫比例估计降低了5%（相当于约8000次调度）。研究还表明，限制在同一运营部门内的重新部署政策可能会带来更有前景的响应时间表现。此外，最佳政策包括在救护车退出服务时进行重新部署以及重新安置基地中闲置救护车的组合。
本研究展示了基于ADP的近似建模框架的成功应用，该框架利用新加坡EMS系统的详细DES模型生成近似最优的动态重新部署计划。评估了与新加坡EMS系统相关的各种政策和情景。
