An optimization approach for ambulance location and the districting of the response segments on highways
In this paper we present a method to optimize the configuration and operation of medical systems on highways. Different from the approaches studied in the previous papers, the present method can support two combined configuration decisions: the location of ambulance bases along the highway and the districting of the segments. For example, this method can be used to make decisions regarding the optimal location and coverage areas of ambulances in order to minimize mean user time or remedy an imbalance in ambulance workloads within the system. The approach is based on embedding a well-known spatially distributed queueing model (hypercube model) into a hybrid genetic algorithm to optimize the decisions involved. To illustrate the application of the proposed method, we utilize two case studies on Brazilian highways and validate the findings via a discrete event simulation model. [Copyright &y& Elsevier]/nCopyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Multiple dispatch hypercube model developedSimulation with Arena (of case study 1)
Case study
Mean user timeImbalance of ambulance workloadsFraction of calls not serviced within a time limit
For simulation on case study 1: The procedures to calculate the transient period (warm up) and the simulation run length are described in detail in Iannoni and Morabito
In this paper we present a method to optimize the configuration and operation of medical systems on highways
The methods would require a prohibitive amount of CPU time to solve problems with more than 10 ambulance.
Our study showed that the main performance measures(objectives), such as the mean user time, imbalance of ambulance workloads, and the fraction of calls not serviced within a time limit could be improved by relocating the ambulance bases and simultaneously determining the district (atom) sizes of the system.
Case 1:EMS provides medical treatments on a portion of an interstate highway connecting the cities of Sao Paulo and Rio de JaneiroCase 2:This EMS has five fixed bases along the two busy highways which intersect, and each base has one ambulance.
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