Models that can predict the correct logistics actions that need to be taken to ensure the availability of spare parts during maintenance of railway vehicles are of considerable interest. Since the occurrence of defects is a random phenomenon it is not possible to know in advance what spare parts will be required at any point in time. This situation requires the creation of a stochastic model that can incorporate uncertainty. Deterministic and stochastic models of maintenance logistics are based on the assumption of a non-zero stock level of spare parts. Carrying an extensive inventory of parts requires a large financial outlay and it is still often the case that a required spare part is not immediately available and has to be obtained. This paper shows, provided certain conditions are met, that it is possible to create cost-effective maintenance procedures even if the required spare part is not immediately available. Thus, the amount of money locked up in stock can be effectively regulated, thus reducing operating costs.
The goal of every public transport operator is to not only provide high-quality service, but also to minimize investment and operating costs. A significant proportion of the costs are associated with the acquisition, operation and maintenance of reserve vehicles. If the number of vehicles held in reserve is too high, an operator will incur economic losses because vehicles are underused. Conversely, if the number of reserve vehicles is too low, the quality of service will be reduced due to disruptions in the timetable. To determine the number of vehicles to hold in reserve, a coefficient of availability is commonly used. The coefficient of availability is determined by two parameters: reliability and maintainability. Both parameters have the same physical dimensions, the mean time between failures (MTBF) and mean time to repair, and they are expressed in hours. Vehicle wear, which results during the emergence of failures, is not very dependent on the time of operation but is strongly dependent on the distance travelled. It is therefore appropriate to use mean distance between failures (MDBF) instead of MTBF, because it better describes the reliability of vehicles. Using MDBF, however, means that the coefficient of availability is not considered, because MDBF and MTTR have different physical dimensions. This problem is solved by using a random vector that makes it is possible to determine the number of vehicles to hold in reserve based on the distance travelled and maintenance time. This original approach allows the acquisition of better and more authentic data necessary for an operator's decision-making process. Therefore, the number of required reserve vehicles can be much better planned. Ultimately, this positively affects the quality of services and also the investment and operating costs of the operator.
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