关键词: 不确定需求, 混合车队, 多中心联合配送, 混合改进差分进化-自适应大邻域搜索

Abstract: In addressing the multi-depot cold chain logistics vehicle routing problem under uncertain demand and mixed fleets, this paper comprehensively considers practical factors such as customer service levels, time windows, multi-depot settings, and joint distribution with electric and fuel vehicles. The goal is to establish a routing optimization model that minimizes the total costs, encompassing vehicle fixed costs, charging costs, fuel costs, cargo damage costs, refrigeration costs, and penalty costs. This model is deterministically transformed by using chance-constrained programming. An initial solution is generated by using the labeling method, and a hybrid improved differential evolution adaptive large neighborhood search (ALNS) algorithm is designed to solve the model. The mutation operator is designed based on a dynamic mutation strategy, and three destruction operators and three repair operators from the ALNS algorithm are introduced for search optimization. The validity of the model and algorithm is verified through practical data and case studies, and the influences of the demand variation coefficient, the number of distribution centers, and the maximum vehicle load capacity on distribution costs are analyzed. This provides a reference for enterprises to rationally allocate transportation resources and optimize decision-making in distribution schemes.

Key words: uncertain demand, mixed fleet, multi-depot joint distribution, hybrid improved differential evolution adaptive large neighborhood search