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Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses

机译:与电动和传统公共汽车混合的公交车队的车辆和充电调度的协作优化

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摘要

To address sustainable development issues of urban traffic, electric buses will join traditional bus system, and the scheduling of bus fleet should be adjusted due to the distinct features of electric buses. To this end, this paper develops a Multi-objective Bi-level programming model to collaboratively optimize the vehicle scheduling and charging scheduling of the mixed bus fleet under the operating conditions of a single depot. The upper level determines the vehicle scheduling to minimize the operating cost and carbon emissions under the constraints of connecting time between trips and the limited driving range of electric buses. The lower level is a charging scheduling problem that considers the charging time and the limited driving distance constraint to minimize the charging cost. The proposed model is solved with an integrated heuristic algorithm. The vehicle scheduling problem is addressed with the iterative neighborhood search algorithm based on simulated annealing, while the charging scheduling problem is solved with a greedy dynamic selection strategy based on the approach of multi-stage decision. Finally, case study is carried out based on a mixed bus fleet in Beijing, and the results validate the availability of the proposed model and solution algorithm.
机译:为了解决城市交通的可持续发展问题,电动公共汽车将加入传统的公交系统,并且由于电动公共汽车的特点而应调整公交车队的调度。为此,本文开发了一种多目标双级编程模型,以协同优化在单个仓库的操作条件下混合总线车队的车辆调度和充电调度。上层确定车辆调度,以最小化在跳闸之间连接时间和电动总线的有限驾驶范围之间连接时间的制约的运行成本和碳排放。较低级别是充电调度问题,其考虑充电时间和有限的驱动距离约束,以最小化充电成本。所提出的模型以综合启发式算法解决。利用基于模拟退火的迭代邻域搜索算法来解决车辆调度问题,而基于多阶段决定的方法,通过贪婪的动态选择策略来解决充电调度问题。最后,案例研究是基于北京混合公交车队进行的,结果验证了所提出的模型和解决方案算法的可用性。

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