Managing EMS Systems with User Abandonment in Emerging Economies
Marla, Lavanya, Kaushik Krishnan and Sarang Deo, "Managing EMS Systems with User Abandonment in Emerging Economies", Accepted July 2020. https://doi.org/10.1080/24725854.2020.1802086
41 Pages Posted: 2 Aug 2018 Last revised: 10 Aug 2020
Date Written: February 28, 2019
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). As a result, ambulance capacity, which is already scarce, is wasted in serving calls that are likely to be abandoned later. In this paper, we investigate the design of an ambulance system in the presence of abandonment behavior, using a two-step approach. 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. Second, we employ a simulation-based optimization approach to explicitly incorporate customers' willingness to wait in: (a) tactical short-term decisions such as modification of dispatch policies and ambulance allocations at existing base locations; and (b) strategic long-term network design decisions of increasing fleet size and re-designing base locations. We calibrate our models using data from a major metropolitan city in India where historically 81.3% of calls were successfully served without being abandoned. We find that modifying dispatch policies or reallocating ambulances provide relatively small gains in successfully served calls (around 1%). By contrast, increasing fleet size and network re-design can more significantly increase the fraction of successfully served calls with the latter being particularly more effective. Redesigning bases with the current fleet size is equivalent to increasing the fleet size by 8.6% at current base locations. Similarly, adding 29% more ambulances and redesigning the base locations is equivalent to doubling the fleet size at the current base locations and adding 34% more ambulances and redesigning base locations is equivalent to a three-fold increase. Our results indicate that in the absence of changes in behavioral factors, significant investment is required to modify operational factors by increasing fleet size, and to modify infrastructural factors by redesigning base locations.
Keywords: Emergency Medical Systems, Abandonment, Maximum Likelihood Estimation, Interval Censoring, Data-Driven Simulation, Greedy Algorithm
Suggested Citation: Suggested Citation