The maintenance scheduling and location choice problem for railway rolling stock
研究了铁路车辆段维护调度与位置选择问题，提出基于逻辑Benders分解的优化框架，通过四种割生成方法提高求解效率，并在荷兰铁路实际数据上验证了减少容量违规的效果。
• Introduction of a model for Maintenance Scheduling and Location Choice • Use of Logic-Based Benders’ Decomposition for faster, scalable solutions • Four cut generation methods tested with min-cut the fastest for suboptimal results • Binary search cuts are best for solving the problem with hard shifts to optimality • Tests on real-world data show a significant reduction in capacity violations The increasing train traffic over railway networks stretches the demand for capacity of railway yards and rolling stock maintenance locations, which increasingly limits performance and further growth. 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 considering the available capacity of maintenance locations. Solving the MSLCP using one large Mixed Integer Programming appears not to perform well enough. Therefore, 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 an MLCP solution. Within the LBBD, four variants of 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 realistic scenarios from the Dutch railways. It is shown that the best choice for the 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.