关键词: 绿色供应链, 纯电动货车, 时变网络, 生产配送协同, 改进蚁群遗传算法

Abstract: Aiming at the problem of how to reduce the power consumption of pure electric trucks in urban distribution under the time-varying network environment, a green production and distribution scheduling model is proposed to optimize the production and distribution links in the supply chain, with speed change as the key variable and power consumption as one of the optimization objectives. The model takes speed change as the key variable, electricity consumption as one of the optimization objectives, and considers constraints such as vehicle dynamic load, service time, and customer demand time window. The ant colony genetic algorithm has been improved by adding heuristic rule learning to the initial population; the Metropolis sampling criterion is used to enhance the algorithm’s ability to jump out of local optima; power consumption factors, completion time factors, and path length factors are introduced to improve the directionality of the algorithm’s evolution. Simulation examples show that the model can effectively reduce vehicle electricity consumption during the distribution process in the supply chain, and the effectiveness of the algorithm is also verified.

Key words: green supply chain, pure electric trucks, time-varying network, production and distribution collaboration, improved ant colony genetic algorithm