Solving the Fuzzy Transportation Problem by a Novel Particle Swarm Optimization Approach

Abstract

The fuzzy transportation problem (FTP) represents a significant extension of the classical transportation problem (TP) by introducing uncertainly and imprecision into the parameters involved. Various algorithms have been proposed to solve the FTP, including fuzzy linear programming, metaheuristic algorithms and fuzzy mathematical programming techniques combined with artificial neural networks. This paper presents the application of trigonometric acceleration coefficients-PSO (TrigAC-PSO) to solve the FTP. TrigAC-PSO is a variation of the classical particle swarm optimization algorithm, which has already been applied to solve the TP showing remarkable success. This fact constitutes the main reason that drives the utilization of TrigAC-PSO in current contribution to further investigate its performance in solving the FTP. TrigAC-PSO’s adaptability to handle fuzzy data by solving the FTP via instances with classic fuzzy numbers and generalized fuzzy numbers is explored through a comprehensive comparison between TrigAC-PSO and established methods applied to solve the FTP. The comparative analysis, with recent state-of-the-art algorithms, demonstrates the efficiency and robustness of the proposed method in solving the FTP across various scenarios. Through experimental results and performance metrics, the superiority of the proposed method is presented by achieving optimal solutions. The innovation of current research contributes to advancing the field of fuzzy optimization while providing variable insights into the application of TrigAC-PSO in real-world scenarios.