An iterative improvement approach for high-speed train maintenance scheduling

Abstract

The high-level maintenance plan is the train-set overhaul arrangement that is scheduled annually and manually in China. Previous studies have investigated this issue based on deterministic daily mileages. However, future daily mileages are difficult to predict, which may cause existing plans to be inaccurate and unfeasible in practice. Therefore, this study only considers the historical daily mileages as raw inputs, and the future daily mileages are considered as ranges to generate wider maintenance time windows for train-sets. Subsequently, a 0–1 integer linear programming model for high-level maintenance scheduling is formulated. After obtaining a high-level maintenance plan, the planned daily mileages of all train-sets are calculated and verified. To make all planned daily mileages feasible, we design an iterative algorithm to adjust the time windows and update the plan. A real-world case study is conducted using the data of 124 CRH2 EMU train-sets belonging to China Railway Shanghai Group to prove the effectiveness of the model and the algorithm. The commercial solver Gurobi is used to solve this case. A program supporting high-level maintenance scheduling has been developed, and it has been used by planners for testing.