Increasing the efficiency of operation and management of railroad transport infrastructure based on maximum levels of fault tolerance

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.311829

Keywords:

technological reliability, railroad transport system, rolling stock, simulation modeling, discrete-event simulation

Abstract

This paper considers the optimization of parameters for a railroad transport system. The maximum level of technological reliability and the average time spent by trains on the route are used as optimization criteria. The purpose of the study is to establish the optimal parameters for the operational process of railroad transport systems according to the criterion of the maximum level of technological reliability and the minimum time of trains on the route. Methods of technological reliability research have been proposed. Taking into account that the entire technological process is a sequential set of technological elements, a simulation model of the technological process of the transit transport-technological line along a route direction has been built. A population of agents that simulates the operation of railroad sections of the rotation of train locomotives and is a key subsystem of the simulation model has been developed and configured. The simulation model makes it possible to optimize the parameters of multi-section railroad lines. This approach is provided owing to the agent approach. As a result of the experiments, the optimal parameters of the functioning of railroad lines were established when organizing the passage of transit trains. The coefficient of utilization of the locomotive fleet fluctuates within the optimal range (0.55–0.65), which indicates the sufficiency of traction resources in the railroad system. The optimal parameters of the railroad transport system were established experimentally using the example of a train flow of 85 pairs of trains on a two-track route with five sections. The problem of "abandoned trains" has a solution but, to this end, it is necessary to increase the fleet of train locomotives by 150–200 % relative to existing standards. At the same time, even with an unlimited fleet of train locomotives, there is a fairly high probability (up to 30–50 %) of technological failures

Author Biographies

Oleksandr Gorobchenko, State University of Infrastructure and Technologies

Doctor of Technical Sciences

Department of Electromechanics and Rolling Stock of Railways

Viacheslav Matsiuk, National University of Life and Environmental Sciences of Ukraine

Doctor of Technical Sciences

Department of Transport Technologies and Means of Agro-Industrial Complex

Halyna Holub, State University of Infrastructure and Technologies

PhD

Department of Automation and Computer-Integrated Transport Technologies

Igor Gritsuk, Kherson State Maritime Academy

Doctor of Technical Sciences

Department of Operation of Ship Power Plants

Oleksandr Nevedrov, State University of Infrastructure and Technologies

Doctor of Philosophy (PhD)

Department of Electromechanics and Rolling Stock of Railways

References

  1. Tirachini, A., Inostroza, F., Mora, R., Cuevas, D., Fuchser, D. (2024). Externalities from the confinement of a railway: Analysis of the barrier effect. Case Studies on Transport Policy, 17, 101225. https://doi.org/10.1016/j.cstp.2024.101225
  2. Tian, A.-Q., Wang, X.-Y., Xu, H., Pan, J.-S., Snášel, V., Lv, H.-X. (2024). Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement. Energy, 294, 130927. https://doi.org/10.1016/j.energy.2024.130927
  3. García-Jiménez, E., Poveda-Reyes, S., Malviya, A. K., Molero, G. D., Santarremigia, F. E. (2023). A methodological framework for a quantitative assessment of new technologies to boost the interoperability of railways services. Transportation Research Procedia, 72, 821–828. https://doi.org/10.1016/j.trpro.2023.11.473
  4. Matsiuk, V., Ilchenko, N., Pryimuk, O., Kochubei, D., Prokhorchenko, A. (2022). Risk assessment of transport processes by agent-based simulation. 13th International Scientific Conference on Aeronautics, Automotive and Railway Engineering and Technologies (BulTrans-2021), 2557, 080003. https://doi.org/10.1063/5.0105913
  5. Gorobchenko, O., Nevedrov, O. (2020). Development of the structure of an intelligent locomotive DSS and as-sessment of its efectiveness. Archives of Transport, 56 (4), 47–58. https://doi.org/10.5604/01.3001.0014.5517
  6. Butko, T., Babanin, A., Gorobchenko, A. (2015). Rationale for the type of the membership function of fuzzy parameters of locomotive intelligent control systems. Eastern-European Journal of Enterprise Technologies, 1 (3 (73)), 4–8. https://doi.org/10.15587/1729-4061.2015.35996
  7. Comi, A., Hriekova, O. (2024). Managing last-mile urban freight transport through emerging information and communication technologies: a systemic literature review. Transportation Research Procedia, 79, 162–169. https://doi.org/10.1016/j.trpro.2024.03.023
  8. McAslan, D., Kenney, L., Najar Arevalo, F., King, D. A., Miller, T. R. (2024). Planning for uncertain transportation futures: Metropolitan planning organizations, emerging technologies, and adaptive transport planning. Transportation Research Interdisciplinary Perspectives, 24, 101055. https://doi.org/10.1016/j.trip.2024.101055
  9. Ma, F., Yu, D., Xue, B., Wang, X., Jing, J., Zhang, W. (2023). Transport risk modeling for hazardous chemical transport Companies – A case study in China. Journal of Loss Prevention in the Process Industries, 84, 105097. https://doi.org/10.1016/j.jlp.2023.105097
  10. Matsiuk, V., Galan, O., Prokhorchenko, A., Tverdomed, V. (2021). An Agent-Based Simulation for Optimizing the Parameters of a Railway Transport System. ICTERI. Kherson. Available at: https://icteri.org/icteri-2021/proceedings/volume-1/20210121.pdf
  11. Stassen, W., Tsegai, A., Kurland, L. (2023). A Retrospective Geospatial Simulation Study of Helicopter Emergency Medical Services’ Potential Time Benefit Over Ground Ambulance Transport in Northern South Africa. Air Medical Journal, 42 (6), 440–444. https://doi.org/10.1016/j.amj.2023.07.005
  12. Sharma, P., Herminghaus, S., Heuer, H., Heidemann, K. M. (2024). Impact of the density of line service stations on overall performance in Bi-modal public transport settings. Multimodal Transportation, 3 (3), 100118. https://doi.org/10.1016/j.multra.2023.100118
  13. Kagho, G. O., Meli, J., Walser, D., Balac, M. (2022). Effects of population sampling on agent-based transport simulation of on-demand services. Procedia Computer Science, 201, 305–312. https://doi.org/10.1016/j.procs.2022.03.041
  14. Namazov, M., Matsiuk, V., Bulgakova, I., Nikolaienko, I., Vernyhora, R. (2023). Agent-based simulation model of multimodal iron ore concentrate transportation. Machinery & Energetics, 14 (1), 46–56. https://doi.org/10.31548/machinery/1.2023.46
  15. Ji, H., Wang, R., Zhang, C., Yin, J., Ma, L., Yang, L. (2024). Optimization of train schedule with uncertain maintenance plans in high-speed railways: A stochastic programming approach. Omega, 124, 102999. https://doi.org/10.1016/j.omega.2023.102999
Increasing the efficiency of operation and management of railroad transport infrastructure based on maximum levels of fault tolerance

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Published

2024-10-30

How to Cite

Gorobchenko, O., Matsiuk, V., Holub, H., Gritsuk, I., & Nevedrov, O. (2024). Increasing the efficiency of operation and management of railroad transport infrastructure based on maximum levels of fault tolerance. Eastern-European Journal of Enterprise Technologies, 5(3 (131), 55–65. https://doi.org/10.15587/1729-4061.2024.311829

Issue

Section

Control processes