考虑交通限行约束的绿色城市物流配送多能源车辆路径优化研究

There is a huge contradiction between explosive growth of urban logistics demands and ecological improvement for urban air pollution and traffic congestion. With the government vigorously supports for the popularization of electric vehicles, it is becoming an important transport solution for urban logistics distribution. In this project, firstly, the transportation and emission cost for both electric and conventional vehicles are analyzed from both the economic and environment considerations. Secondly, under the traffic restriction constraint, we raise a vehicle type dependent minimal cost path problem, and then make modeling and design fast heuristic algorithms based on Dijkstra algorithm. Thirdly, based on the technical characteristics of electric vehicles, we propose some scheduling strategies, e.g., allowing multi-trip for electric vehicles, assigning specific customers with priority to electric vehicles. The mixed integer linear programming model is established aiming to minimize both cost and carbon emission for the multiple energy fleet vehicle routing problem, and exact algorithms are designed based on CPLEX to solve the small size problem in a short time. Finally, considering the complexity of this problem, we develop variable neighborhood search based heuristic algorithms to solve large problem, and evaluate their effectiveness and efficiency in real data from practice. The contribution of our project is that it provides decision-making supports for improving the urban logistics efficiency, reducing economic and environment cost, and promoting electric vehicle applications in green city logistics distribution.

城市物流需求剧增与空气污染和交通拥堵之间存在着巨大的矛盾，在国家大力推广和政策支持下，电动汽车逐渐成为城市物流配送的重要运输工具。基于电动汽车技术和成本的特点，以及交通限行的约束，本课题首先从经济和环保双重因素分析电动汽车和传统能源车的运输和碳排放成本。其次，提出交通限行约束下的两点间车型依赖最小成本路径模型，并基于Dijkstra算法设计快速启发式算法；第三，根据电动汽车的技术特点，提出允许电动汽车多车程，优先服务特定客户群的调度策略，建立以总运输成本和碳排放成本最小的多能源车辆路径优化的混合整数规划模型，并基于CPLEX设计精确算法求解小规模的问题。最后，基于变邻域搜索设计能够在可接受时间内有效求解大规模问题的智能优化算法，并利用企业调研数据验证算法的有效性。本课题的研究成果，将为提高城市物流配送效率，降低经济和环境成本，推广电动汽车在绿色城市物流配送中的应用提供决策支持和理论依据。

近年来，在国家的大力推广和政策支持下，电动汽车逐渐成为城市物流配送的重要运输工具。电动汽车与传统燃油车有不同的技术特性和运营成本，并在限行政策下具有优先路权。如何利用电动汽车的这些特点进行科学路径规划与调度成为物流企业运营的重要问题。本项目研究了以下几种相关问题：（1）基于多车程的带时间窗电动汽车车辆路径问题；（2）多配送中心带时间窗电动汽车车辆路径问题；（3）可在客户处充电的带时间窗电动汽车车辆路径问题；（4）限行条件下多能源c多车型车辆路径问题。对上述四类问题进行深入研究，建立了数学规划模型，并设计了基于自适应大规模邻域搜索算法进行求解。基于数值实验验证了模型和算法的有效性。此外，对延伸出的扩展问题也进行了研究，包括新能源移动充电车的路径问题和基于超停的共享电动汽车再平衡调度问题。本课题的研究成果将丰富车辆路径优化领域的理论研究，并能够为推广电动汽车在绿色城市物流配送中的应用提供决策支持和理论依据。