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An adaptive large neighborhood search heuristic for the Electric Vehicle Scheduling Problem

机译:一种自适应大邻域搜索启发式的电动车辆调度问题

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摘要

This paper addresses the Electric Vehicle Scheduling Problem (E-VSP), in which a set of timetabled bus trips, each starting from and ending at specific locations and at specific times, should be carried out by a set of electric buses or vehicles based at a number of depots with limited driving ranges. The electric vehicles are allowed to be recharged fully or partially at any of the given recharging stations. The objective is to firstly minimize the number of vehicles needed to cover all the timetabled trips, and secondly to minimize the total traveling distance, which is equivalent to minimizing the total deadheading distance. A mixed integer programming formulation as well as an Adaptive Large Neighborhood Search (ALNS) heuristic for the E-VSP are presented. ALNS is tested on newly generated E-VSP benchmark instances. Result shows that the proposed heuristic can provide good solutions to large E-VSP instances and optimal or near-optimal solutions to small E-VSP instances.
机译:本文解决了电动车辆调度问题(E-VSP),其中一组时间发布总线跳闸,每个总线跳闸以及在特定位置和特定时间结束的时间,都应通过一组电动总线或基于基于的车辆来执行许多有限的驱动范围的仓库。允许电动车辆完全或部分地在任何给定的再充电站中充电。目的是首先最小化覆盖所有时间发行跳闸所需的车辆数量,其次是最小化总行进距离,这相当于最小化总死头距离。呈现了混合整数编程制定以及E-VSP的自适应大邻域搜索(ALNS)启发式。 ALNS在新生成的E-VSP基准实例上进行测试。结果表明,拟议的启发式可以为大型E-VSP实例和小型E-VSP实例提供良好的解决方案和最佳或接近最佳解决方案。

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