A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

McCormack, R.J.; Coates, G.

Abstract

An effective emergency medical service (EMS) is a critical part of any health care system. This paper presents the optimization of EMS vehicle fleet allocation and base station location through the use of a genetic algorithm (GA) with an integrated EMS simulation model. Two tiers to the EMS model realized the different demands on two vehicle classes; ambulances and rapid response cars. Multiple patient classes were modeled and survival functions used to differentiate the required levels of service. The objective was maximization of the overall expected survival probability across patient classes. Applications of the model were undertaken using real call data from the London Ambulance Service. The simulation model was shown to effectively emulate real-life performance. Optimization of the existing resource plan resulted in significant improvements in survival probability. Optimizing a selection of one hour periods in the plan, without introducing additional resources, resulted in a notable increase in the number of cardiac arrest patients surviving per year. The introduction of an additional base station further improved survival when its location and resourcing were optimized for key periods of service. Also, the removal of a base station from the system was found to have minimal impact on survival probability when the selected station and resourcing were optimized simultaneously.

Citation

McCormack, R., & Coates, G. (2015). A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival. European Journal of Operational Research, 247(1), 294-309. https://doi.org/10.1016/j.ejor.2015.05.040

• Journal Article Type: Article
• Acceptance Date: May 17, 2015
• Online Publication Date: May 23, 2015
• Publication Date: May 23, 2015
• Deposit Date: May 20, 2015
• Publicly Available Date: May 23, 2017
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Electronic ISSN: 1872-6860
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 247
• Issue: 1
• Pages: 294-309
• DOI: https://doi.org/10.1016/j.ejor.2015.05.040
• Keywords: Simulation, Optimization, Emergency medical service.
• Public URL: https://durham-repository.worktribe.com/output/1437717