2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)
The IEEE Russia North West Section, IEEE Russia Section, Saint Petersburg Electrotechnical
University “LETI”, National Research University of Electronic Technology "MIET", and
Glyndwr University, UK are pleased to present the Proceedings of the 2019 IEEE Conference of
Russian Young Researchers in Electrical and Electronic Engineering (2019 ElConRus) held in
St. Petersburg and Moscow, Russia on January 28 - 30, 2019. This conference is proudly hosted
by two universities - Saint Petersburg Electrotechnical University “LETI” and the National
Research University of Electronic Technology "MIET". The Organising Committee believes
and trusts that we have been true to the spirit of collegiality that members of IEEE value whilst
also maintaining a high standard as we reviewed papers, provided feedback and now present a
strong body of published work in this collection of proceedings.
The themes for this year's conference were chosen as a means of bringing together the many
orientations of electrical and electronic engineering research and teaching, and providing a basis
for discussion of issues arising across the young engineering community in relation to electrical
and electronic engineering.
The aim in these proceedings has been to present high quality work in an accessible medium, for
use in the teaching and further research of all people associated with electrical and electronic
engineering studies. To achieve this aim, all abstracts were blind reviewed, and full papers
submitted for publication in this journal of proceedings were subjected to a rigorous reviewing
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.