Imperialist Competitive Algorithm to the Emergency Medical Services Location Problem

Minimization of arrival time at scenes plays an essential role to help injured people in emergency events. This can be undertaken through mathematical programming models, called emergency medical services location problem, and solved by conventional exact algorithms or by recent meta-heuristic methods as well. Meta-heuristic algorithms have recently been realized to be more efficient in the sense of computing times especially in large-scale cases. The emergency medical services location problem would be further complicated when the number of stations and/or emergency vehicles, as an important indicator of system costs, should be determined at the same time. In this paper, a newly introduced optimization method, Imperialist Competitive Algorithm (ICA), is used to solve the EMS location problem. The ICA mimics the human's socio-political evolution to solve continuous problems. In this paper, a discrete version of the ICA is sought to be adapted to solve the EMS location problem. The adapted ICA algorithm is then applied on two benchmark problems with four different demand scenarios as well as on the real transportation network of Mashhad City. Results of this algorithm are compared with those of other wellknow meta-heuristic algorithms (i.e. the genetic algorithm, the simulated annealing and the particle swarm optimization). These results indicate that the cpu time of the ICA is averagely less than that obtained from the other algorithms, and the number of required ambulances is not considerably different.