Optimization of high-level preventive maintenance scheduling for high-speed trains. (March 2019)

• Record Type: Journal Article
• Authors: Lin, Boliang; Wu, Jianping; Lin, Ruixi; Wang, Jiaxi; Wang, Hui; Zhang, Xuhui
• Is Part Of: Reliability engineering & system safety. Volume 183(2019)
• Page Start: 261
• Page End: 275
• Publication Date: 2019-03
• Subjects: High-speed railway -- EMU train -- High-level maintenance planning -- 0-1 programming -- Simulated annealing; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.11.028
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A non-linear piecewise state function is designed to describe maintenance states. A high-level maintenance planning model for high-speed trains is formulated. The original model is linearized using a big M method. Develop a simulated annealing algorithm to solve the relaxation model. Conduct a real-world case study with 124 trains from the China Shanghai Railroad. Abstract: For safety reasons, a high-speed train needs to carry out the preventive maintenance when its accumulated running mileage or time reaches a predefined threshold. This paper formulates the train high-level preventive maintenance planning problem as a 0-1 programming model. A novel state function is designed to describe whether a train is under maintenance or not. By using this function, the constraint for restricting the total number of trains under maintenance can be formulated reasonably well. A linearization technique is also employed to refine the original non-linear state function into a linear one. To handle large-scale instances, a simulated annealing algorithm is proposed for solving the problem and is applied to a real-world case study from Shanghai Railway Bureau (SRB), a regional railway operator under China Railway. The optimized results yield a total cost of 3, 619, 200 standard train-km in terms of remaining mileage. In addition, sensitivity analyses based on the real-world case study reveal some interesting insights. We have delivered the results to SRB as a useful and efficient decision support tool for their high-level maintenance planning work.