VarMOPSO: multi-objective particle swarm optimization with variable population size
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In this paper, the use of evolutionary metaheuristics for the optimization of emergency medical services (EMS) applied to a real-world case in Argentina is analyzed. The problem requires the simultaneous optimization of two opposing objectives -- reducing service delay time and minimizing the use of third-party medical vehicle. Therefore, a multiobjective technique was implemented. Several multiobjective techniques that had good results reported in the literature were assessed. The techniques that presented the best indicators in this case were selected. Also, a disturbance operator that improves the results found by the assessed algorithms was developed. The objectives were achieved. A process to dispatch medical vehicles to home medical services based on evolutionary computing was successfully carried out, maximizing the use of the available installed capacity, improving response time rates and using a smaller amount of resources.