We present a Variable Neighborhood Search heuristic for the rolling stock rescheduling problem. Rolling stock rescheduling is needed when a disruption leads to cancellations in the timetable. In rolling stock rescheduling, one must then assign duties, i.e., sequences of trips, to the available train units in such a way that both passenger comfort and operational performance are taken into account. For our heuristic, we introduce three neighborhoods, which focus on swapping duties between train units, on improving the individual duties and on changing the shunting that occurs between trips, respectively. These neighborhoods are used for both a Variable Neighborhood Descent local search procedure and for perturbing the current solution in order to escape from local optima. Moreover, we show that the heuristic can be extended to the setting of flexible rolling stock turnings at ending stations by introducing a fourth neighborhood. We apply our heuristic to instances of Netherlands Railways (NS). The results show that the heuristic is able to find high-quality solutions within 1 ​min of solving time. This allows rolling stock dispatchers to use our heuristic in real-time rescheduling.

我们提出了一种可变邻域搜索启发式方法，用于机车车辆的重新调度问题。当中断导致时间表取消时，需要对机车车辆进行重新计划。然后，在机车车辆的重新安排中，必须以一种兼顾乘客舒适度和运行性能的方式，将职责（即行程顺序）分配给可用的火车单元。对于我们的启发式方法，我们引入了三个街区，分别侧重于列车单元之间的职责互换，改善个人职责和改变行程之间发生的分流。这些邻域既可用于可变邻域下降本地搜索过程，也可用于扰乱当前解决方案，以逃避局部最优。而且，我们表明，通过引入第四个邻域，可以将启发式方法扩展到终端机站灵活的机车车辆车削的设置。我们将启发式方法应用于荷兰铁路（NS）实例。结果表明，启发式算法能够在求解时间的1分钟内找到高质量的解决方案。这使机车车辆调度员可以使用我们的启发式方法进行实时重新调度。