Location of Ambulance Bases for the Attention of Traffic Accidents in Mexico City
DOI:
https://doi.org/10.4108/eai.13-7-2018.164180Keywords:
Emergency services, Bi-level programming, Benders Decomposition, LocationAbstract
INTRODUCTION: This paper studies the problem of choosing the location of motorcycle ambulance bases in Mexico City, seeking to have an effective response to traffic accidents. In order to quickly attend the requirements of the attention of accidents, it is desirable to have as much infrastructure as possible. However, this implies high costs.
OBJECTIVES: This document addresses the problem of locating ambulance bases and determining the number of emergency vehicles in order to respond effectively to traffic accidents.
METHODS: The problem considers criteria that imply two levels of decision that control the installation of the bases and the deployment of ambulances, respectively. The decision on the location leads the hierarchical process and decides the installation of bases considering the costs associated with the opening. Deployment of ambulances, which is the next in the decision process, is responsible for assigning the accidents that will be attended to by the units. With both decisions involved, we suggest a two-level program to model the problem. The model is solved through an algorithm based on the decomposition of Benders.
RESULTS: We formulated the problem as a bi-level programming problem. The model was used to locate ambulance bases in Mexico City. In solving the model, We identified five bases to cover 95% of the city’s accidents.
CONCLUSIONS: We developed a model to locate motorcycle ambulance bases and obtained a proposal for the allocation of services. We proposed a bi-level programming model that is a good approach to solving the problem since it considers two decisions that are made hierarchically. The goal was to minimize the total cost associated with installed bases and patient care.
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This work is licensed under a Creative Commons Attribution 3.0 Unported License.
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Funding data
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Universidad Nacional Autónoma de México
Grant numbers IN116217