The paper is concerned with the schedule optimization problem in the railway control systems. The schedule optimization problem has been formulated as a problem of finding the global extreme of the fitness function. Authors propose 2 different methods for the problem solving using mathematical optimization technologies, namely stochastic optimization algorithm, and the genetic algorithm respectively.
A description of the use of artificial intelligence in the development of decision support systems, which are used for various types of transport, is given. These systems are aimed at restructuring the schedule of movement of objects due to unforeseen deviations from the preplanned schedules. Machine learning of a neural network using a genetic algorithm is used. This minimizes the functionality that characterizes the deviation from the given schedule.
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