共同优化站点位置、行程分配和电动公交车充电时间表的影响

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
Rito Brata Nath , Tarun Rambha , Maximilian Schiffer
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引用次数: 0

摘要

随着全球许多公共交通系统向电动公交车过渡,通过量身定制的决策支持工具可以改善车队的规划和运营。在这项工作中,我们研究了共同确定充电设施位置、将电动公交车分配到各个班次以及确定何时何地为公交车充电的影响。我们提出了一种混合整数线性程序,可共同优化规划和运营决策,并提出了一种迭代局部搜索启发式来解决大规模实例。在此,我们使用并发调度算法生成一个初始可行解,作为我们迭代局部搜索算法的起点。在顺序情况下,我们首先优化行程分配和充电位置。然后,在确定第一层的最优决策后,再确定充电时间表。另一方面,联合模型将充电调度整合到局部搜索程序中。我们比较了联合模型和顺序迭代局部搜索模型在多个实际公交网络中的求解质量。结果表明,与顺序模型相比,联合模型有助于进一步提高运营成本 14.1%,总成本平均降低约 4.1%。此外,由于采用了综合规划方法,能耗成本和合同电力容量成本也大幅降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the impact of co-optimizing station locations, trip assignment, and charging schedules for electric buses

As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed integer linear program that co-optimizes planning and operational decisions jointly and an iterated local search heuristic to solve large-scale instances. Herein, we use a concurrent scheduler algorithm to generate an initial feasible solution, which serves as a starting point for our iterated local search algorithm. In the sequential case, we first optimize trip assignments and charging locations. Charging schedules are then determined after fixing the optimal decisions from the first level. The joint model, on the other hand, integrates charge scheduling within the local search procedure. The solution quality of the joint and sequential iterated local search models are compared for multiple real-world bus transit networks. Our results demonstrate that joint models can help further improve operating costs by 14.1% and lower total costs by about 4.1% on average compared with sequential models. In addition, energy consumption costs and contracted power capacity costs have been reduced significantly due to our integrated planning approach.

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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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