A two-stage optimization approach for inspection plan formulation of comprehensive inspection train: The China case

Abstract The regular and dynamic inspections of high-speed railways (HSR) are carried out by comprehensive inspection train (CIT). Generally, the network of HSR (e.g., the HSR network in China) has some obvious characteristics such as complex network structure, high traffic density and strict maintenance restrictions of CIT, which make the formulation of CIT inspection plan extremely hard. However, formulating and optimizing the CIT inspection plan is one of the key issues to ensure the safety of HSR operations and to improve the efficiency of testing equipment. In this paper, the original model is proposed for the multiple CITs inspection plan formulation problem (MCIT-IPFP) firstly, and it is proved that the problem belongs to the NP-hard class of computational complexity by reduction. By decomposing the problem into two stages, one is used to divide the HSR network evenly for each CIT, and the other one is used to generate the path, the timetable, and maintenance base assignment for each CIT under requirements of reducing the total time cost and the difficulty of solving. In order to improve the efficiency of calculation, a Constructive Heuristic Algorithm with feedback regulation based on the greedy algorithm is designed to generate the task division plan. With the purpose of quickly formulating the inspection plans with low time cost for each CIT with Euler circuit characteristics, a simulated annealing algorithm with efficient neighborhood Euler circuit generation strategy is proposed. Finally, a case study is conducted to test the efficiency of the algorithm by taking the China’s HSR network as the background. Compared with the previous manual inspection plan, the utilization rate of CITs will increase by 30%-50% when using the models and algorithms proposed in this paper.