Improved biogeography-based optimization using migration process adjustment: An approach for location-allocation of ambulances

Abstract

Emergency centers are noticeably necessary for all societies, and allocation population to such centers is an important optimization issue in urban planning. This paper investigates the maximum coverage location problem (MCLP) in order to provide the optimal condition for ambulances locations through improved biogeography-based optimization (IBBO). The four-dimensional migration model consists of the host, guest, elite, and random habitats has been designed to improve the neighborhood search and random search of BBO. In order to evaluate the performance of the developed algorithm, BBO, GA and PSO algorithms are utilized. Also, to validate the results of meta-heuristics, CPLEX solver is used in the GAMS software. Geographic information system (GIS) and the analytical hierarchy process (AHP) are applied to select some candidate sites and restrict the search space. Moreover, a binary genetic algorithm has been used to determine the minimum number of required centers for the construction of urban emergency centers. Ultimately, the suggested algorithms have been implemented to recognize 9 optimal locations of real data in district 2 of Tehran. In general, the IBBO results in the best answer in 90% of scenarios, which has a substantially high convergence rate. The average execution time of the algorithms indicates that the spent time by IBBO is lower than the other three algorithms. As well, the algorithms have been evaluated in three types of random instances and the obtained results of IBBO have shown a high-performance capability. This paper shows that the proposed method provides significant results in determining the appropriate locations for emergency centers such that the population coverage is maximized.