Optimization of high-level preventive maintenance scheduling for high-speed trains

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.

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