Comparing Metaheuristic Optimization Algorithms for Ambulance Allocation: An Experimental Simulation Study

被引:3
|
作者
Schjolberg, Magnus Eide [1 ]
Bekkevold, Nicklas I. Paus [1 ]
Sanchez-Diaz, Xavier F. C. [1 ]
Mengshoel, Ole Jakob [1 ]
机构
[1] Norwegian Univ Sci & Technol, Trondheim, Norway
来源
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023 | 2023年
关键词
vehicle fleet management; ambulance allocation; emergency medical service; response time; simulation; optimization; genetic algorithms; stochastic local search; memetic algorithms; STOCHASTIC LOCAL SEARCH; LOCATION; MODEL; TRANSPORTATION; DEPLOYMENT; DEMAND;
D O I
10.1145/3583131.3590345
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The optimization of Emergency Medical Services is a central issue in modern healthcare systems. With this in focus, we study a data set containing medical emergencies for the years 2015-2019 from Oslo and Akershus, Norway. By developing a discrete trace-based simulation model based on the data set, we compute average response times that are used to optimize ambulance allocations to stations in the region. We study several metaheuristics, specifically genetic, stochastic local search, and memetic algorithms. These metaheuristics are tested using the simulation to optimize ambulance allocations, considering response times. The algorithms are compared against each other and a set of baseline allocation models over different time periods. The main results of our experimental simulation study are that: (i) the metaheuristics generally outperform the simpler baselines, (ii) the best-performing metaheuristic is the genetic algorithm, and (iii) the performance difference between the metaheuristics and the simpler baselines increases in situations with high demand on ambulances. Finally, we present suggestions for future work that may help to further improve upon the current state-of-the-art.
引用
收藏
页码:1454 / 1463
页数:10
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