Penerapan Algoritma Particle Swarm Optimization untuk Penentuan Rute Distribusi pada Traveling Salesman Problem

Abstract

Distribution plays a crucial role in increasing profits, and selecting an suboptimal distribution route can lead to increased distribution costs. UMKM Cutella Presto Jember faces challenges in determining the shortest distribution route for a single delivery cycle, resulting in inefficient travel distances. This study aims to determine the shortest distribution route for UMKM Cutella Presto Jember by modeling the problem as a Traveling Salesman Problem (TSP) and solving it using the Particle Swarm Optimization (PSO) algorithm. The data used includes the locations of and distances between distribution points. The distance between distribution points was then calculated using Google Maps and arranged into a distance matrix. The PSO algorithm was implemented to solve the TSP using Python software. This implementation involved converting the continuous position values into a route sequence, calculating the total distance, and evaluating the fitness value at each iteration. This study conducted tests on key PSO parameters, including the number of particles, the number of iterations, the inertia weight, and the acceleration coefficients, to identify the optimal parameter configuration. The parameter test demonstrates that the combination of these parameters has a significant influence on the resulting total distance. The results show that the optimal configuration yields a total distance of 32,75 km. This result demonstrates that the PSO algorithm produces a more efficient solution than the current route, with a difference of 2,03 km