LAUSR

In the context of increasing construction of new railways and uninterrupted operation of existing railways in China, maintenance supervisors are facing great challenges to achieve as much maintenance workloads as possible in an efficient and economical way within the required maintenance cycle. Therefore, a cost-effectiveness oriented intelligent maintenance scheduling optimization method for the traction power supply system (TPSS) of high-speed railways (HSRs) is proposed. First, the skill levels of maintenance operators are evaluated by the skill evaluation model, and the workflow model of TPSS maintenance tasks is established to describe the relation between the operator deployment and the maintenance efficiency. Then the bi-objective optimization model which simultaneously maximizes maintenance workload and minimizes cost subject to available resources during a prescribed period is established. A modified bi-objective genetic algorithm is applied to obtain the Pareto solutions of the model and thus optimized maintenance scheduling strategies are formulated. A case study dealing with the maintenance scheduling optimization of a catenary system in China shows that, compared with the current scheme, the proposed strategy can effectively improve maintenance efficiency and meanwhile reduce cost. Furthermore, a prototype of an intelligent maintenance scheduling system (IMSS) for HSR TPSS is developed. The application of IMSS with field data indicates it can help operators formulate highly efficient yet economical maintenance strategies, and meanwhile motivate the transition of current maintenance decision-making policy from traditional experience-based strategies towards scientific “cost-effective” ones.