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Today’s railroad passenger wagons experience a large frequency of failures due to a large number of usage hours, and consequently planning maintenance is complex and expensive. Due to high maintenance costs, special attention should be given to the planning of part storage. This study develops a model which by using the data collected in the maintenance process, serves in optimising the costs of stock parts for a set level of reliability. The data on drive schedules and wagon repairs, mean time between failures and maintenance costs were collected over a year by a rail carrier for its entire rolling stock of passenger wagons. The first phase of developing the model using regression methods provided mathematical models of reliability dependency in terms of train use time and costs of spare parts for groups of passenger wagons grouped into technical characteristics. In the second phase, the problem of optimal stocks was solved by applying linear programming methods. The newly obtained model can be applied in practice as a tool for optimising the required stocks of parts based on a set level of equipment reliability and set maintenance costs.

New reliability-based model of stock optimisation for railroad passenger wagon maintenance