Optimizing Urban Electric Concrete Mixer Truck Routing with an Improved Ant Colony Algorithm: Balancing Costs and Discharge Profits

Abstract

Urban traffic restrictions and the growing demand for low-carbon logistics have created new challenges for the routing of electric concrete mixer trucks in urban distribution. To address this issue, this study investigates the route planning problem of electric concrete mixer trucks under constraints related to customer demand, battery capacity, and vehicle load. A mixed-integer programming model is developed for a one-to-many urban distribution scenario with the objective of minimizing total distribution cost, including fixed vehicle cost, driver time cost, and charging cost. On the basis of the model structure, an improved ant colony algorithm incorporating enhanced search and pheromone-update strategies is designed to solve the problem efficiently. To examine the effectiveness of the proposed approach, numerical experiments are conducted on randomly generated test instances representing radial and circular customer distribution patterns. The computational results show that fixed vehicle usage cost and time-based usage cost are the dominant components of total distribution cost, accounting on average for 56.03% and 42.47%, respectively, whereas charging cost contributes only 1.50%. In addition, the results suggest that discharge behavior during the distribution process may provide supplementary economic benefits under time-dependent electricity pricing conditions, with average discharge profit reaching 57.22 yuan. The improved ant colony algorithm is able to generate feasible route plans for different spatial distribution scenarios within acceptable computation time, with an average runtime of 252.10 s. Overall, this study provides a useful modeling and solution framework for the cost-oriented planning of electric concrete mixer truck routing in urban logistics.