Development of the structure of an intelligent locomotive DSS and assessment of its effectiveness

The purpose of the article is developing the locomotive structure of intellectual system of support of decision-making and to find a criterion by which to adequately assess different control action to the train. System of decision support for locomotive crew is seen as a complex structure with complex interactions located at a great distance, on-board locomotive systems. The quality of the organization determines the effectiveness of the system as a whole. To solve the problem of creating the optimal structure of the DSS applies the aggregate-decomposition method that involves two steps: decomposition of the problem into a number of subproblems and aggregating the partial results. To evaluate the quality control of a locomotive used the concept of control strategy with specific indicators. Design is developed and structure of locomotive DSS is obtained, taking into account peculiarities of operation of railway transport. To account for not only quantitative but also qualitative characteristics of activity of the locomotive or intellectual systems of decision support, it is proposed to use methods of fuzzy logic. So were able to deduce and calculate the additive criterion of the quality control activities of the intelligent system. Formal indicator of the quality of the train control process using different strategies is received. In the work theoretically grounded definition of the weighting factors for each partial criterion of the quality of train control. Using the dependencies derived, the nature of the influence of the value of partial criteria on the quality of train control in relation to a strategy. The results of the work allow to more accurately simulate the operations of a locomotive crew, which in the future will serve as the basis for the development of autonomous intelligent systems of locomotive control. The developed method is shown to be three main criteria which values the safety, energy consumption, and execution time schedule. However, for more flexible and accurate model, this approach allows to enter additional criteria, and the simplicity of the calculation provides the necessary speed when implemented on on-board locomotive computers.