Purpose: The train formation plan is a fundamental technological document that affects the rational organization of the operational work of railways of JSCo “Russian Railways”. The amount of operating costs, the stability of the operation of stations, sections and transport hubs depends on its correct development. The existing practice assumes the use of the average values of its design parameters when calculating the train formation plan, without taking into account their variable nature. In this case, it is not always possible to guarantee that the optimal decision is taken on the organization of car traffic. The purpose of the article is to describe a method for taking into account the variable nature of the design parameters of the train formation plan, which will increase the stability of its individual assignments to the unevenness of operational work. Methods: Methods of theoretical analysis are used, existing analytical dependencies are described, which allow determining the calculated parameters of the train formation plan. Fuzzy set theory and its natural extension, fuzzy mathematics, have been used to describe the variable nature of the calculated parameters. To make an optimal decision on the adjustment of individual assignments of the train formation plan, the methods of decision-making theory have been used, namely, the Bellman-Zadeh method of decision-making in fuzzy conditions. Results: Calculation formulas have been obtained that allow determining the design parameters of the train formation plan in conditions of uneven operational work directly, without using auxiliary tables and graphs, having information only about the fluctuations of the car traffic of a specific purpose of the formation plan. A method has been developed for determining the threshold value of the car traffic, optimal in conditions of unevenness. Practical significance: Taking into account the variable nature of the design standards of the train formation plan during its development will increase the stability of its specific assignments, reduce operating costs for its implementation during the life cycle, reduce the number of operational adjustments.
