Aisha Nasywa Emiliana, Rudi Nurdiansyah
This paper presents a comparative evaluation of the Bat Algorithm (BA) and Bee Colony Optimization (BCO) for solving the asymmetric Travelling Salesman Problem (TSP) using empirical delivery data from 155 locations in Malang, Indonesia. In the current logistics practice, delivery routes are often selected randomly without considering the shortest path, which leads inefficient and longer delivery times due to unnecessarily travel distances. Both algorithms are run in MATLAB and operate under identical experimental conditions over ten independent trials. The results show that BCO consistently outperforms BA by producing shorter and more reliable routes with a minimum tour length of 31,700 meters, compared to BA minimum of 33,110 meters. BCO's superior performance results from its adaptive exploration and exploitation mechanisms. These findings validate the effectiveness of swarm intelligence techniques, particularly BCO, in optimizing last-mile logistics. © 2025 IEEE.
Universitas Negeri Malang, Department of Mechanical and Industrial Engineering, Malang, Indonesia
