The Aplication of Genetic Algorithms for the Scheduling of Electric Rolling Stock Maintenance
This paper discusses the application of genetic algorithms for the scheduling of electric rolling stock maintenance. The main objective is to improve the automated train scheduling system of uniformity maintenance process with a variety of maintenance resources, including the limited resources. The methods of graph theory and Bellman principle allow us to get the entire set of suitable maintenance schedules and choose which maintenance corresponds to the train schedule, and the minimum differs from the optimal one according to the selected criteria. Traditionally, it takes a significant amount of time and the main problem is using the criterion of uniformity maintenance under limited resources. In this case, we used genetic algorithm for optimization. The results showed that the genetic algorithm is an effective tool for optimization of maintenance scheduling.