考虑电动汽车电池深度放电的服装门店配送路径规划
Apparel Store Delivery Path Planning Considering Deep Discharge of Electric Vehicle Batteries

为了在国家“双碳”战略下实现服装行业的绿色配送，提出了考虑电池深度放电的服装门店配送路径规划模型。该模型按服装特性分为两种不同的装载方式同时考虑车厢三维特征装车，采用了“分级定价”策略和深度放电前换电策略，以最小化固定成本、折旧和维护成本、换电成本、慢充成本构成的总运输成本为目标函数。为求解该模型，提出一种GA-ACO算法，并使用上海某知名服装品牌的配送数据，结合上海市的实际路网进行了仿真实验。实验结果表明算法能够有效求解该模型，为实例中的17个门店构造了3条配送路径，且在配送中完成1次换电。在分级定价策略下，总运输成本与不采取深度放电前换电策略相比降低3.63%，其中换电成本降低65.3%，能有效减少电池损伤，延长电池使用寿命。
Under the context of China’s national “Dual Carbon” strategy, a routing optimization model for green logistics in the fashion industry has been developed that accounts for battery deep discharge. This model segments clothing based on their attributes is divided into two unique loading patterns while concurrently considering the three-dimensional packing features within cargo compartments. It employs a “Tiered Pricing” strategy alongside a proactive battery swapping scheme before deep discharge occurs, aiming to minimize the total transportation cost, which is comprised of fixed costs, depreciation and maintenance expenses, battery swapping fees, and slow charging costs. To solve this complex model, an GA (Genetic Algorithm) ant colony optimization (GA-ACO) algorithm has been put forward. This algorithm was applied using distribution data from a prominent fashion brand in Shanghai, integrated with the city’s actual road network focal analysis. The outcomes revealed that the algorithm efficiently solved the model, successfully devising 3 distribution routes for the 17 stores in the case study, with one battery swap taking place during the course of deliveries. In the tiered pricing structure, it was found that the overall transportation cost was reduced by 3.63% compared to scenarios without the adoption of the proactive battery swapping prior to deep discharge, battery swapping costs plummeted by 65.3%, effectively mitigating battery wear and tear and extending battery lifespan.