Railroad classification yards serve as central hubs in single wagonload freight transportation by disassembling inbound trains and classifying outbound ones. This enables railcars to switch trains, thereby reducing the number of point-to-point connections for low demand origin-destination pairs. The quality of the operations in classification yards has a large impact on the overall performance of the system. The planning process comprises the cut generation problem, the train makeup problem, the railcar classification problem, the outbound track assignment problem, and the scheduling of service and safety operations along with the assignment of both locomotives and staff to them. This tactical planning task is nowadays mainly done manually by experienced planners and most optimization models in the literature focus only on subproblems. In the hope of filling this gap, we therefore propose a formulation for the integrated planning problem in classification yards. Since the formulation turns out to be intractable for general-purpose solvers, we propose a tailored adaptive large neighborhood search heuristic that yields high-quality results for realistic instances. Problems with up to 20 inbound and outbound trains are solved on average in less than 20 min with an average optimality gap of 0.5% for the instances for which an optimal solution is known.

铁路分级场作为单车货物运输的中心枢纽，对进站列车进行拆解，对出站列车进行分级。这使轨道车能够换乘列车，从而减少低需求起点-终点对的点对点连接数量。分级场的操作质量对系统的整体性能有很大影响。规划过程包括切割生成问题、列车编组问题、轨道车分类问题、出站轨道分配问题、服务和安全操作的调度以及机车和工作人员的分配。现在，这种战术规划任务主要由经验丰富的规划人员手动完成，文献中的大多数优化模型仅关注子问题。为了填补这一空白，我们因此提出了分类场综合规划问题的公式。由于该公式对于通用求解器来说是难以处理的，因此我们提出了一种量身定制的自适应大型邻域搜索启发式方法，可以为现实实例产生高质量的结果。最多 20 列进站和出站列车的问题平均在 20 分钟内解决，对于已知最优解的实例，平均最优差距为 0.5%。