Dynamic dispatch policies for emergency response with multiple types of vehicles

Soovin Yoon and Laura A. Albert

Transportation Research Part E: Logistics and Transportation Review, 2021, vol. 152, issue C

Abstract: Emergency medical service (EMS) systems have two main goals when sending ambulances to patients: rapidly responding to patients and sending the right type of personnel to patients based on their health needs. We address these issues by formulating and studying a Markov decision process model that determines which type of ambulances (servers) to send to patients in real-time. The base model considers a loss system over a finite time horizon, and we provide a model variant that considers an infinite time horizon and the average reward criterion. Structural properties of the optimal policies are derived. Computational experiments using a real-world EMS dataset show that the optimal policies inform how to dynamically dispatch ambulance types to patients. We propose and evaluate three classes of heuristics, including a static constant threshold heuristic, a greedy heuristic, and a dynamic greedy threshold heuristic. Computational results suggest that the greedy threshold heuristic closely approximates the optimal policies and reduces the complexity of implementing dynamic policies in real settings.

Keywords: Markov decision processes; Tiered emergency medical services; Ambulance dispatching (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.tre.2021.102405

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