状态修模式下重载铁路车底运用与检修计划一体化优化方法研究OA

The operation and maintenance scheduling of rolling stock is a critical component of heavy-haul railway transportation planning.Heavy-haul railways typically organize transportation and maintenance activities using fixed trainsets,and the application of condition-based maintenance(CBM)has increased the complexity of maintenance scheduling.This study aimed to optimize the planning of Z1-level maintenance for rolling stock under the CBM mode,ensuring timely maintenance of defective rolling stock while improving the utilization efficiency of maintenance resources.To achieve this,transportation tasks and maintenance tasks were abstracted as nodes,while rolling stock connections and maintenance connections were modeled as arcs,forming a rolling stock operation and maintenance connection network for heavy-haul railways.On this basis,the idle time of maintenance lines was introduced as a metric to evaluate maintenance resource utilization efficiency.Two optimization objectives were proposed:minimizing the number of trainsets and minimizing maintenance line idle time,subject to constraints such as rolling stock connection time standards,maintenance operation conditions and capacity limits,type consistency,and loaded-empty cycle connection.Accordingly,an integrated multi-objective optimization model for rolling stock operation and maintenance scheduling was established.Given the characteristics of the model,various neighborhood construction rules were defined,and a non-dominated sorting mechanism was incorporated to design a multi-objective large-scale neighborhood search(MOLNS)algorithm to solve the model.A case study based on a regional heavy-haul railway network in China was conducted to validate the model and algorithm.The computational results show that the proposed approach yields a set of Pareto-optimal solutions that balance the number of trainsets and maintenance line idle time.Compared with traditional planning methods,the Pareto solution set reduces the number of trainsets by 1.4%～6.3%and decreases maintenance line idle time by 21.5%～46.3%.Moreover,it enables up to four additional postponed maintenance tasks to be scheduled within the same period,demonstrating significant optimization effectiveness.The proposed method provides a valuable reference for optimizing rolling stock operation and Z1-level maintenance planning under the condition-based maintenance mode in heavy-haul railways.