Comparing the performance of Genetic Algorithm and Particle Swarm Optimization Algorithm in allocating and scheduling fire stations for dispatching forces to a fire/accident (A Case study: the Region 19, Tehran, Iran)

Abstract Considering the importance of "time" in the process of dispatching forces to reach the fire or accident site, GA or PSO models can be used as artificial intelligence alternatives. Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSOA) models can be used. This research shows which of these two models is more appropriate in this case study. With the hypothesis that GA and PSOA have positive effects on the allocation and scheduling of the stations, this research seeks to compare them in order to find which one of these two methods is more appropriate to shorten the time to reach fire/incident site in the Region 19 of Tehran. This is an applied type of research. Data analysis was carried out using NFPA standards and MATLAB software. The statistical population includes 8 fire stations and 250 personnel of the stations selected in a simple way, and the sampling volume was obtained using Morgan's table (n = 148). At first, the algorithm of dispatching forces to reach the site of fire/incident was designed and implemented based on PSOA, GA and the time to response the incident according to NFPA 1720 standards. After writing the assumptions of the problem and running the mathematical model from nonlinear to linear, the data was entered into the MATLAB software, and finally by comparing the performance improvement of PSOA and GA, appropriate results were obtained. In order to efficiently assign and schedule fire stations to arrive at the site, a linear numerical programming model was presented with the aim of minimizing the arrival time and taking into account the effect of firemen's fatigue (α = 0.1). The findings of the research showed that the operation processing time (of fire extinguishing) had a normal distribution with a mean of 40 minutes and a variance of 10 minutes, independent of the severity of the incident. Also, fatigue coefficient was calculated 0.1 by analyzing the sensitivity of the solution time of the algorithm with changes [0–1]. The initial standard travel time, with an average speed of 47 km/h and a density factor of 1.24, was 5 min :20 s . Solving the problem in large and small dimensions showed that the initial power effect of each fire station is 0.36 according to the fatigue level of the forces. Based on the obtained results, GA performs better in terms of problem solution time, and the improved PSOA also has higher quality answers.