The Optimization of Path Planning for Express Delivery Based on Clone Adaptive Ant Colony Optimization

Abstract

In recent years, China's express delivery market has developed rapidly in the context of a booming economy. However, logistics costs are still high, which will affect the decision-making and policy making of relevant departments. Therefore, it is essential to optimize the last-mile assignment problem (LMAP) to meet the consumer's demand for delivery time and reduce economic expenditure. The LMAP of express delivery requires multiple packages to be delivered to different destinations. Finding the path with the minimum delivery cost and time is an NP-hard problem, and it is impossible to obtain the optimal solution by enumerating all possible answers. This study proposes a new express delivery path planning method based on a clone adaptive ant colony optimization (CAACO) to find suboptimal solutions. Moreover, a new distribution cost fitness function constructed by weighing the economic expenditure and time of express delivery is designed. Specifically, a new adaptive operator and a novel clone operator are also designed to accelerate the speed of convergence. Finally, by comparing the performance of CAACO with ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA), the effectiveness of CAACO in solving the express LAMP is verified. In the simulation results, it is obvious that the economic expenditure and time of express delivery based on the CAACO are lower than ACO, SA, and GA, and the convergence speed is also faster than the SA and GA. It can be seen that CAACO has valuable benefits in solving LMAP.