Presenting a multi-objective model to develop depot location through particle swarm optimization algorithm in Artawheel Tire Company

This study aimed to present a multi-objective model for the location of depots through a particle swarm optimization algorithm in Artawheel Tire Company. It is applied in terms of purpose and survey and descriptive in terms of the nature of research and data collection. Data collection tools are documents and interviews with experts. Also, the research is of the predictive type in proportion to the fact that the research seeks to locate the depot using the particle swarm optimization algorithm. Given that this problem falls into the category of Hard-PN problems, a meta-heuristic method based on the particle swarm optimization algorithm is used to solve it. Two particle group optimization algorithms and genetics have been used as benchmark algorithms to evaluate the performance of the proposed algorithm. The proposed particle cluster optimization algorithms are implemented in the Matlab 7.5 programming software and the genetic algorithm is implemented using the Matlab 7.5 software toolbox. According to the results of this study, it was found that the use of a particle swarm algorithm to solve the problem of vehicle routing can improve the objective function as well as the total number of routes travelled by vehicles.