Abstract:

In view of the shortcomings of the research on electric vehicle routing optimization in combination with charging station and vehicle sharing, this paper proposes several optimization strategies, including the charging station and electric vehicle sharing with multiple service periods and centralized transportation among multiple centers. In addition, it studies the electric vehicle routing optimization problem of multi-center joint distribution based on resource sharing. First, it establishes a bi-objective optimization model to minimize the operating cost, including the rental cost of electric vehicles, the cost of electricity consumption, the cost of service, the penalty cost for violating the time windows, and the number of electric vehicles. Then, it designs a 3D-K-means spatial-temporal clustering algorithm based on the characteristics of the model considering the geographic location and demand time windows of customers. Next, it proposes a hybrid algorithm consisting of the Clarke-Wright (CW) saving algorithm and the multi-objective particle swarm optimization (CW-MOPSO). It uses the CW saving algorithm to generate the initial solutions, and devises the charging station insertion strategy, the external archive update strategy, and the resource sharing strategy in MOPSO to improve the quality of Pareto optimal solutions. Afterwards, it verifies the proposed CW-MOPSO algorithm by comparing it with the non-dominated sorting genetic algorithm, the multi-objective genetic algorithm, and the multi-objective gradient evolution algorithm. Finally, it conducts a case study of the electric vehicle routing optimization problem of multi-center joint distribution based on the resource sharing modes with the actual data of a logistics enterprise in Chongqing, China. It analyzes and discusses the changes of the operating cost, the number of electric vehicles, the electricity consumption, and the number of used charging stations of the multi-center joint distribution network under the conditions of the uncertainty of the waiting time of electric vehicles in the charging station, the stepwise relationship between the electric vehicle power consumption and speed, and the different resource sharing modes. The results show that in the scenario where some charging stations queue up and the rest do not queue up, some electric vehicles will choose a charging station with a longer distance for charging in order to reduce the waiting time in the queue, which increases the driving distance of electric vehicles and has a higher penalty cost for customer delay. However, when the electric vehicles have different speed states in different service time periods, the power consumption of electric vehicles in the process of long-distance constant speed driving is less than that of short-distance driving in different speed states. Moreover, the proposed model and algorithm can achieve the sharing of charging stations, the shared scheduling of electric distribution vehicles, and the reasonable electric vehicle routing optimization. Furthermore, the proposed model and algorithm can improve the operation efficiency of the multi-center joint distribution network and reduce the operating cost, thus providing theoretical support and decision-making reference for urban logistics and distribution enterprises to realize the rational configuration of charging stations and optimal scheduling of the electric distribution vehicles.

Key words:

resource sharing mode, multi-center joint distribution, electric vehicle routing, charging station, Clarke-Wright- multi-objective particle swarm optimization (CW-MOPSO) hybrid algorithm

摘要：

针对电动车辆路径优化在充电站共用和车辆共享相结合方面存在的不足，提出充电站和电动车在多服务时间段内共享以及多中心间的集中运输调度策略，研究了基于资源共享的多中心共同配送电动车辆路径优化问题。首先，构建了包含电动车租赁成本、电量消耗成本、服务成本、违反时间窗惩罚成本的运营成本最小化和电动车使用数最小化的双目标优化模型。其次，根据模型特点设计了考虑客户地理位置和需求时间窗的3D-K-means时空聚类算法，提出一种基于Clarke-Wright（CW）节约算法的多目标粒子群（MOPSO）混合算法（CW-MOPSO）。该混合算法集成了CW节约算法生成的初始解，并在MOPSO中设计了充电站插入策略、外部存档更新策略和资源共享策略，提高了帕累托优化解的质量。再次，与非支配排序遗传算法、多目标遗传算法和多目标梯度进化算法进行了对比分析，验证了CW-MOPSO混合算法的有效性。最后，结合重庆某物流企业的实际数据对资源共享模式下多中心共同配送电动车辆路径优化问题进行研究，探讨了电动车在充电站的排队等待时间存在不确定性、电动车电量消耗和速度呈阶梯性关系以及不同资源共享模式下多中心共同配送的运营成本、电动车使用数、电量消耗和充电站使用数等指标的变化情况。研究结果表明：部分充电站排队、其余充电站不排队场景下存在部分电动车为了减少排队等待时间会选择较远距离的充电站进行充电的情景，进而增加了电动车的行驶距离并存在较高的客户延迟服务惩罚成本；而当不同服务时间段电动车具有不同速度状态时，电动车的电量消耗在长距离匀速行驶过程中比多次不同速度状态短距离行驶的电量消耗更少。此外，本研究所提出的模型和算法能够实现多中心共同配送线路中充电站的共享、电动配送车辆的共享调度以及合理的电动车辆路径优化，并在有效提高多中心共同配送网络运营效率的同时降低物流运营成本，为城市物流配送企业进行充电站的合理化配置和电动配送车辆路径优化调度提供理论支撑和决策参考。

关键词:

资源共享模式, 多中心共同配送, 电动车辆路径, 充电站, CW-MOPSO混合算法