Abstract: With the requirements of green and sustainable development, the use of electric logistics vehicles for cold chain logistics and distribution has gradually become a hotspot. Under the consideration of constraints such as charging strategy, vehicle loading, and customer time window, a path optimization model of electric vehicles in cold chain logistics and distribution with the objective of minimizing the total cost of distribution is constructed. According to the characteristics of the designed model, a hybrid algorithm combining marine predator and ant colony algorithm is proposed for solving, which effectively improves the searching ability and global information capture. According to the comparison of the analyses of the algorithms, it can be seen that considering the charging strategy and thus not charging the vehicle to full charge can reasonably utilize the vehicle resources and effectively reduce the logistics cost by 17.34% compared with the fully charging strategy. It analyzes the impact of maximum vehicle load on the total logistics cost by setting the maximum vehicle load, so as to provide enterprises with different vehicle choices. By utilizing actual cases and specific data for experiments, this verifies that the model constructed is effective and proves the efficiency of the algorithm.

Key words: urban traffic, vehicle routing problem (VRP), ant colony optimization (ACO), marine predators algorithm (MPA), charging strategy

摘要： 随着绿色与可持续发展要求的提出，使用电动物流车进行冷链物流配送逐渐成为热点，在考虑充电策略、车辆载重和客户时间窗等约束下，构建了配送总成本最小化为目标的电动车辆在冷链物流配送中的路径优化模型。根据设计的模型特点，提出了一种结合海洋捕食者和蚁群算法的混合算法进行求解，该算法有效提高了搜索能力和全局信息捕捉。根据算例分析对比可知，与完全充电策略相比，采用部分充电策略可以提高配送效率，有效降低了17.34%物流成本。通过设置车辆最大载重和不同充电站排队时间，分析车辆最大载重和充电站排队时间对物流总成本的影响，从而为企业提供不同车辆的选择。用实际案例和具体数据进行实验，验证了构建模型的有效性，证明了算法的高效性。

关键词: 城市交通, 车辆路径问题（VRP）, 蚁群算法（ACO）, 海洋捕食者算法（MPA）, 充电策略