Due to increasing railway use, the capacity at railway yards and maintenance locations is becoming limiting. Therefore, the scheduling of rolling stock maintenance and the choice regarding optimal locations to perform maintenance is increasingly complicated. This research introduces a Maintenance Scheduling and Location Choice Problem (MSLCP). It simultaneously determines maintenance locations and maintenance schedules of rolling stock, while it also considers the available capacity of maintenance locations, measured in the number of available teams. To solve the MSLCP, an optimization framework based on Logic-Based Benders' Decomposition (LBBD) is proposed by combining two models, the Maintenance Location Choice Problem (MLCP) and the Activity Planning Problem (APP), to assess the capacity of a MLCP solution. Within the LBBD, four cut generation procedures are introduced to improve the computational performance: a naive procedure, two heuristic procedures and the so-called min-cut procedure that aims to exploit the specific characteristics of the problem at hand. The framework is demonstrated on a realistic scenarios from the Dutch railways. It is shown that the best choice for cut generation procedure depends on the objective: when aiming to find a good but not necessarily optimal solution, the min-cut procedure performs best, whereas when aiming for the optimal solution, one of the heuristic procedures is the preferred option. The techniques used in the current research are new to the current field and offer interesting next research opportunities.
翻译:由于铁路的使用增加,铁路场和保养地点的能力日益受到限制,因此,车辆维修的时间安排和对最佳维修地点的选择越来越复杂。这项研究提出了维修安排和地点选择问题(MSLCP),同时确定车辆维修地点和保养时间表,同时考虑维修地点的现有能力,以现有小组的数量来衡量。为了解决MSLCP, 提出了一个基于逻辑的Benders分解(LBBD)的优化框架,办法是将维护地点选择问题(MLCP)和活动规划问题(APP)这两个模型合并起来,以评估刚果解放运动解决方案的能力。在LBBD中,采用四个切割一代程序来改进计算性能:一种天真的程序、两个超大程序以及所谓的小管程序,目的是利用现有问题的具体特点。荷兰铁路对这个框架作了现实的演示。 事实证明,裁员程序的最佳选择取决于目标:在寻找一个良好但不一定最理想的解决方案时,在目前的研究过程中,采用最理想的办法是采用最佳的实地方法。