Simulation-based decision support framework for dynamic ambulance redeployment in Singapore. (October 2017) - Lam, Sean Shao Wei; Ng, Clarence Boon Liang; Nguyen, Francis Ngoc Hoang Long; Ng, Yih Yng; Ong, Marcus Eng Hock
Graphical abstract: Highlights: An Approximate Dynamic Programming (ADP) based framework that leverage upon a high-resolution Discrete Events Simulation (DES) model of the Singapore's EMS system to generate dynamic deployment plans has been proposed in this study. The dynamic deployment plans derived from this framework can improve on the coverage of ambulances, with an estimated 5% reduction in the proportion of calls that cannot be reached with an 8 min response time threshold. The best policy involved the integration of dynamic ambulance redeployment every time when ambulances are released from service and that of relocating ambulances that are idle in bases. Abstract: Objective: 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. Methods: 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. Results: Considering an 8 min 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. Conclusion: 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.