First, because the callers’ actual willingness to wait for ambulances is censored, we adopt a Maximum Likelihood Estimator estimation approach suitable for interval censored data. In this paper, we investigate the design of an ambulance system in the presence of abandonment behavior, using a two-step approach.
As a result, ambulance capacity, which is already scarce, is wasted in serving calls that are likely to be abandoned later. In many emerging economies, callers may abandon ambulance requests due to a combination of operational (small fleet size), infrastructural (long travel times) and behavioral factors (low trust in the ambulance system). We conduct simulation experiments based on real usage data of an EMS system from a large Asian city, and demonstrate significant improvement in the system's service levels using static allocations and re-deployment policies discovered by our approach. Given its efficiency, we can repeatedly employ this approach in real-time for dynamic reposi-tioning. We derive data-driven performance guarantees which yield small optimality gap. Despite this complexity, we show that embedding our simulator within a simple and efficient greedy al-location algorithm produces good solutions. Futhermore, the utility of any particular allocation can only be measured via a seemingly "black box" simula-tor. In both the static and dynamic settings, this modeling approach leads to an exponentially large ac-tion space (with respect to the number of ambulances). We take a simulation-based approach, where the utility of an al-location is measured by directly simulating emergency requests. We present an efficient approach to ambulance fleet al-location and dynamic redeployment, where the goal is to position an entire fleet of ambulances to base loca-tions to maximize the service level (or utility) of the Emergency Medical Services (EMS) system.