Investigation into the Optimisation of Cold Chain Logistics Distribution Paths using the Hybrid Ant Colony Method

Abstract

China's cold chain logistics market has been growing quickly in recent years. Cold chain logistics helps minimize food loss and waste during transit in addition to meeting people's need for fresh food. As the idea of "green logistics" has gained traction, we created a better ant colony algorithm with a multi-objective heuristic function to address the issue. Specifically, we combined the A* algorithm with the ACO algorithm to address the issue of insufficient pheromone in the early stages of the ACO algorithm, and the resulting improved multi-objective ACO algorithm was able to solve the vehicle path distribution problem with a multi-objective optimisation model more successfully than the traditional ACO algorithm, yielding more Pareto efficient solutions. Ultimately, simulation studies demonstrate that the distribution paths produced by the multi-objective model and algorithm presented in this paper can concurrently optimize for lowering distribution costs, cutting carbon emissions, and raising customer satisfaction, ultimately resulting in a more ecologically friendly and greener distribution solution.