Abstract
This paper studies the issue of uncertainty in the ambulance location problem to cover the maximum number of demand points in a city. The work is based on the double standard model (DSM), a popular coverage model where two radii are considered to cover a percentage of the demand points twice. Uncertainty is introduced in the expected travel time between an ambulance and a demand point, before computing the optimal placement of ambulances in potential bases by solving the linear program posed by the DSM. The following three approaches are considered: (1) solving the DSM without uncertainty; (2) uncertainty in the travel time is based on triangular fuzzy set; and (3) a fuzzy inference system (FIS) with a rule base derived from the problem properties, which is the main contribution of this work. Results show that considering uncertainty can have a significant effect on the solutions for the DSM, with the solutions produced with the FIS approach achieving a higher total coverage of the demand. In conclusion, the proposed strategy could provide a reliable and effective tool to support decision making in the ambulance location problem by considering uncertainty in the ambulance travel times.
Highlights
In recent years, the development of computational support systems for emergency medical services (EMS) has attracted a growing amount of attention from researchers
The ambulance location problem deals with two types of decisions: (a) which sites in a city should be used as bases, and (b) determining how many ambulances should be placed in each base
Models for this problem can be divided into three main groups (Li et al, 2011): (1) covering models that focus on locating ambulances such that the demand can be covered within a certain amount of time; (2) p-median models that focus on minimizing the total distance between ambulance and demand points; and (3) p-center models that minimize the maximum distance between ambulances and demand points
Summary
The development of computational support systems for emergency medical services (EMS) has attracted a growing amount of attention from researchers. Studies have shown that the probability of a patient surviving is reduced by 7–10% with each passing minute in which defibrillation and ALS are not provided, and resuscitation is mostly unsuccessful after 10 min (EMSWorld.com, 2014) Another example is traffic accident, where the number of deaths was estimated to be 1.25 million in 2013. This work is motivated by the lack of resources and their sub-optimal use, which is evident when we consider that the average response time of RCT ambulances was approximately 14 min with an SD of 7 min. Section “Travel Time Estimation for Ambulances” presents how uncertainty in the travel time is modeled in our work, with our main contribution being the use of an FIS. Section “Conclusion and Future Work” outlines the conclusion and describes future work
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