Optimization of Emergency Material Logistics Supply Chain Path Based on Improved Ant Colony Algorithm

Abstract

Path selection is a critical challenge in emergency logistics management, particularly under realistic disaster-related conditions. This study addresses the problem of optimizing logistics transportation during major epidemics, considering constraints such as vehicle load, volume, and maximum travel distance per delivery. The goal is to minimize costs related to distribution trips, time, early/late penalties, and fixed vehicle expenses. By framing the problem as a generalized Traveling Salesman Problem, we developed an Improved Ant Colony Algorithm (IACA) to reduce the longest distribution path. Simulation data from Pudong, Shanghai lockdown zones revealed that IACA outperformed the traditional ACO algorithm, achieving a 30% cost reduction and higher accuracy (R² = 0.98). Additionally, experiments on gate assignment and TSP demonstrated the algorithm's superior optimization ability and stability. Overall, IACA enhances delivery route efficiency, lowers costs and energy consumption, and improves emergency logistics performance, proving to be a robust and reliable solution.