Search Space Reduction in Road Networks for the Ambulance Location and Allocation Optimization Problems: A Real Case Study

Abstract

One particular problem with ambulance location is when, after a disaster, there are no fixed bases for all the available ambulances, and the entity in charge must assign them a temporal location to efficiently provide emergency medical care. Most of the ambulance location problems are modeled as extensions of the set covering problem or the location problem. However, there is no efficient algorithm that can solve them in polynomial time, given that their solutions cannot be applied in emergency environments since they require a quick response. This paper describes a proposal to allocate temporary ambulance bases on streets that meet specific requirements. The contribution of this work is a methodology to reduce the search space of possible locations, simplifying the graph as much as it cans, and thereby will reduce the execution time when an algorithm to determine the locations is applied. The proposal includes a mathematical model integrated with two objective functions; one to minimize the distance between bases and demand points and the other to minimize the number of ambulance bases. This article presents the results of the methodology to reduce the search space, applied in a real-based study case, from 2017 when the strongest earthquake in Mexico’s history, collapsed several buildings in Mexico City. The emergency demand locations are carried out using Geospatial Information Systems (GIS) from an open-source platform. The methodology uses data prepossessing and an optimization algorithm to obtain the best available locations to reach the emergency demand points.