Maintenance schedule optimization based on failure probability distribution

Organizations related to infrastructure, such as utilities and railway companies, manage a large number of facilities, the failure of which can have a huge impact on society. The cost of maintaining these facilities is a combination of regular maintenance costs and urgent recovery costs. Generally, the urgent costs are much higher than regular costs. Regular maintenance work should result in fewer sudden failures, and thus reduce these urgent costs. However, if the regular maintenance is too frequent, its cost becomes too high. Therefore, it is important to balance the regular and urgent costs to minimize the overall maintenance cost. We propose a maintenance schedule optimization method based on the failure probability distribution of the facilities. The total cost is mathematically modeled, with the regular maintenance schedule included via decision variables and the occurrence of failures modeled as stochastic variables. The stochastic total maintenance costs are evaluated using a Monte Carlo method, and a genetic algorithm is employed to optimize the maintenance schedule. The proposed method is evaluated using data provided by a Japanese railway company, and our results confirm that the method produces an excellent maintenance schedule. A statistical test shows there is a significant difference between the proposed and conventional methods.