Modified Ant Colony Optimization with an Application in Sustainable Last-Mile Delivery Routing Problem

Abstract

Sustainable last-mile delivery is a critical part of urban logistics, especially in the context of normalization of epidemic prevention and control. The key of the last-mile delivery problem is to design an optimal set of delivery routes with multi-task logistics unmanned ground vehicles. This paper proposes an improved route planning algorithm that takes the recharging cost and total travel time into account. Specifically, based on the ant colony optimization (ACO) algorithm, the pheromone concentration of each path is updated by using particle swarm optimization (PSO) algorithms, which enhances the global exploring ability of the ACO algorithm. In addition, adaptive factors are introduced to balance the flexibility and stability of the considered algorithm. Finally, the simulation results demonstrate that the modified ACO algorithm has a better performance and can be suited to the sustainable last-mile delivery routing problem.