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Battery swap station location-routing problem with capacitated electric vehicles

机译:电容式电动汽车的电池交换站位置路由问题

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In this paper, we present an electric vehicles battery swap stations location routing problem (BSS-EV-LRP), which aims to determine the location strategy of battery swap stations (BSSs) and the routing plan of a fleet of electric vehicles (EVs) simultaneously under battery driving range limitation. The problem is formulated as an integer programming model under the basic and extended scenarios. A four-phase heuristic called SIGALNS and a two-phase Tabu Search-modified Clarke and Wright Savings heuristic (TS-MCWS) are proposed to solve the problem. In the proposed SIGALNS, the BSSs location stage and the vehicle routing stage are alternated iteratively, which considers the information from the routing plan while improving the location strategy. In the first phase, an initial routing plan is generated with a modified sweep algorithm, leading to the BSSs location subproblem, which is then solved by using an iterated greedy heuristic. In the third phase, the vehicle routes resulting from the location subproblem are determined by applying an adaptive large neighborhood search heuristic with several new neighborhood structures. At the end of SIGALNS, the solution is further improved by a split procedure. Compared with the MIP solver of CPLEX and TS-MCWS over three sets of instances, SIGALNS searches the solution space more efficiently, thus producing good solutions without excessive computation on the medium and large instances. Furthermore, we systematically conduct economic and environmental analysis including the comparison between basic and extended scenarios, sensitivity analysis on battery driving range and efficiency analysis about the vehicle emissions reduction when EVs are used in the logistics practice. (C) 2014 Elsevier Ltd. All rights reserved.
机译:在本文中,我们提出了电动汽车电池交换站的位置路由问题(BSS-EV-LRP),旨在确定电池交换站(BSSs)的位置策略和电动车队(EV)的路由计划同时在电池行驶范围限制内。在基本和扩展方案下,问题被表述为整数编程模型。为解决该问题,提出了一种称为SIGALNS的四阶段启发式算法以及两阶段禁忌搜索修改的Clarke和Wright Savings启发式算法(TS-MCWS)。在拟议的SIGALNS中,BSS的定位阶段和车辆的选路阶段是交替进行的,它在改进选址策略的同时考虑了选路计划中的信息。在第一阶段,使用修改后的扫描算法生成初始路由计划,从而导致BSS的位置子问题,然后使用迭代贪婪启发式法对其进行求解。在第三阶段,通过应用具有多个新邻域结构的自适应大邻域搜索启发式方法,确定由位置子问题产生的车辆路线。在SIGALNS的最后,通过拆分过程进一步改进了该解决方案。与CPLEX和TS-MCWS的MIP求解器在三组实例上相比,SIGALNS可以更有效地搜索解决方案空间,从而在不对中型和大型实例进行过多计算的情况下,产生了良好的解决方案。此外,我们系统地进行经济和环境分析,包括基本情景和扩展情景之间的比较,电池续驶里程的灵敏度分析以及在后勤实践中使用电动汽车时的车辆减排效率分析。 (C)2014 Elsevier Ltd.保留所有权利。

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