The article investigates the possibility to apply genetic algorithms at automation of planned schedules compilation for subway passenger train traffic. Research main goal – to improve automated system of planned schedule compilation for passenger trains for to provide for processes evenness at the use of various resources and consideration of existing limitations. Necessary definitions of resources and limitations on conditions of genetic algorithm usage are narrowed down to interconnected and unified tables. On probability approach basis, the influence of various combinations of genetic algorithm parameter values on population compositions in the process of search for effective results of transportation process planning for urban rail transport system has been studied. For investigation needs, computer software has been performed on high-level languages C# and Python. Genetic algorithm adaptation to the solution of the task for compilation automation of planned schedule of subway passenger train traffic has been made and there has been shown the algorithm applicability to automation of the complex of interconnected tasks for the transportation process planning: electric rolling stock turnout schedule compilation and locomotive team work schedule. There have been calculated probability values to get favorable outcome - the presence of all possible allele values at various combinations of the values of primary population size and allele needed quantity in the results of train traffic planned schedules.
genetic algorithm, fitness-function, evenness criteria, primary population size, python, urban rail transport system, train traffic schedule, transportation process planning