Improved Ant Colony Optimization Algorithm dalam Menyelesaikan Persoalan Rute JNE Cabang Utama Medan Amplas Trade Center

Abstract

Afficient good distributions is a crucial factor in modern logistics services. JNE Cabang Utama Medan Amplas Trade Center faces challenges in determining optimal delivery routes to minimize travel time. This study aim to design a delivery route system based on Improved Ant Colony Optimization (IACO) Algorithm to solve routing problem for JNE Cabang Utama Medan Amplas Trade Center. A total of 48 delivery points, were analyzed and groupinto 15clusters using the K-Means Algorithm, based on gegraphical proximity. Each cluster represents the coverage area for an individual delivery vehicle. IACO was then applied to determine to optimal visiting sequence within each cluster, with the route starting and ending at the depot. The result demonstrate that this approachcan generate delivery routes with a total distance of 4188.6 km, which is more efficient 12.66% than nonoptimal approach. Further experimentation with a 13-vehicle configuration demonstrated even higher efficiency, yielding a distance reduction of 18.33%, lowering the initial total travel distance from 4795.7 km to 3917 km. These results indicate that the 13-vehicle configuration provides superior performance compared to the 15-vehicle configuration. Therefore, the use of 13 vehicles is identified as the most optimal solution, as it delivers the greatest distance savings and enhances the overall distribution efficiency of JNE Medan Amplas Trade Center. Therefore, the IACO algorithm proves to be effective in supporting logistical decision making, particulary in the context of multi-vehicle route planning.