Particle Swarm Optimization (PSO)Based Intelligent System to Optimize Fuzzy Transportation Models

This research introduces an advanced approach employing Particle Swarm Optimization (PSO) for optimizing the fully Fuzzy Transportation Problem (FTP). The primary objective of this study is to develop an intelligent system utilizing PSO to effectively optimize the cost associated with the FTP. In this research, the parameters of supply, demand, and cost are represented using hexagonal fuzzy numbers. Subsequently, the FTP is transformed into a Crisp Transportation Problem (TP) utilizing the range technique. The developed methodology leveraging PSO is seamlessly integrated into MATLAB, enhancing its intelligence. Specifically, when one inputs the crisp TP into this code, the solution is readily obtained. To validate the effectiveness of our proposed method, we conducted a detailed analysis on a numerical problem extracted from existing literature. The performance of our approach was quantitatively evaluated using specific numerical indices, underscoring its superiority over existing methods. Our findings indicate that the proposed approach outperforms existing methods, rendering it suitable for a wide range of classical transportation problems.