Particle-Based Swarm Fuzzy Optimization Approach in Vague Measurement of the Distance in Transportation Problems

This paper addresses the vague measure of distance for transportation problems to improve both time and cost factors. By minimizing the distance between the source and destination points, overall efficiency can significantly be enhanced. This study aims to achieve distance reduction through the utilization of particle swarm fuzzy optimization (FPSO) with the combinations of fuzzy logic techniques. The paper examines the different scenarios to demonstrate the effectiveness of PSO and fuzzy PSO. First, PSO is used to reduce the distance between the source and the conveyances with constant distance. Second, when the conveyance is in motion, PSO is utilized to find the most efficient route and minimize the overall distance travelled. Lastly, in situations involving uncertainty, fuzzy PSO is applied to handle unpredictable variables and optimize the transportation vague distances accordingly. To illustrate the proposed methodology, the study implements the developed approach using MATLAB and described a practical example. The results demonstrate the effectiveness of the fuzzy PSO-based approach in minimizing transportation distances and improving overall transportation efficiency. The discussions in this paper provide valuable insights into the application of PSO and fuzzy logic in transportation optimization.