As fuel prices increase and emission regulations become increasingly strict, electric vehicles have been used in various logistics distribution activities. Most studies have focused on the electric vehicle routing problem under a deterministic environment, neglecting the effects of uncertain factors in practical logistics distribution. Thus, a novel fuzzy electric vehicle routing problem with time windows and recharging stations (FEVRPTW) is investigated in this study, and a fuzzy optimization model is established based on credibility theory for this problem. In the presented model, fuzzy numbers are used to denote the uncertainties of service time, battery energy consumption, and travel time. Moreover, the partial recharge is allowed under the uncertain environment. To solve the model, an adaptive large neighborhood search (ALNS) algorithm enhanced with the fuzzy simulation method is proposed. In the proposed ALNS algorithm, four new removal algorithms are designed and integrated for addressing the FEVRPTW. To further improve the algorithmic performance, the variable neighborhood descent algorithm is embedded into the proposed ALNS algorithm and five local search operators are applied. The experiments were conducted to verify the effectiveness of the proposed ALNS algorithm for solving the presented model.

随着燃料价格的上涨和排放法规的日益严格，电动汽车已用于各种物流配送活动。大多数研究都集中在确定性环境下的电动汽车路线问题上，而忽略了不确定因素在实际物流配送中的影响。因此，本文研究了带有时间窗和充电站的新型模糊电动汽车路径问题（FEVRPTW），并基于可信度理论建立了模糊优化模型。在提出的模型中，模糊数用于表示服务时间，电池能耗和行驶时间的不确定性。此外，在不确定的环境下允许部分充电。为了解决模型，提出了一种基于模糊仿真的增强型自适应大邻域搜索算法。在提出的ALNS算法中，设计并集成了四个新的删除算法来解决FEVRPTW。为了进一步提高算法性能，将可变邻域下降算法嵌入到提出的ALNS算法中，并应用了五个局部搜索算子。进行实验以验证所提出的ALNS算法解决所提出模型的有效性。将可变邻域下降算法嵌入到提出的ALNS算法中，并应用了五个局部搜索算子。进行实验以验证所提出的ALNS算法解决所提出模型的有效性。将可变邻域下降算法嵌入到提出的ALNS算法中，并应用了五个局部搜索算子。进行实验以验证所提出的ALNS算法解决所提出模型的有效性。