Simulation of railway marshalling yards using the methods of the queueing theory
https://doi.org/10.21683/1729-2646-2021-21-3-27-34
Abstract
Aim. The paper primarily aims to simulate the operation of railway transportation systems using the queueing theory with the case study of marshalling yards. The goals also include the development of the methods and tools of mathematical simulation and queueing theory.
Methods. One of the pressing matters of modern science is the development of methods of mathematical simulation of transportation systems for the purpose of analyzing the efficiency, stability and dependability of their operation while taking into account random factors. Research has shown that the use of the most mature class of such models, the singlephase Markovian queueing systems, does not enable an adequate description of transportation facilities and systems, particularly in railway transportation. For that reason, this paper suggests more complex mathematical models in the form of queueing networks, i.e., multiple interconnected queueing systems, where arrivals are serviced. The graph of a queueing network does not have to be connected and circuit-free (a tree), which allows simulating transportation systems with random structures that are specified in table form as a so-called “routing matrix”. We suggest using the BMAP model for the purpose of describing incoming traffic flows. The Branch Markovian Arrival Process is a Poisson process with batch arrivals. It allows combining several different arrivals into a single structure, which, in turn, significantly increases the simulation adequacy. The complex structure of the designed model does not allow studying it analytically. Therefore, based on the mathematical description, a simulation model was developed and implemented in the form of software.
Results. The developed models and algorithms were evaluated using the case study of the largest Russian marshalling yard. A computational experiment was performed and produced substantial recommendations. Another important result of the research is that significant progress was made in the development of a single method of mathematical and computer simulation of transportation hubs based on the queueing theory. That is the strategic goal of the conducted research that aims to improve the accuracy and adequacy of simulation compared to the known methods, as well as should allow extending the capabilities and applicability of the model-based approach.
Conclusions. The proposed model-based approach proved to be a rather efficient tool that allows studying the operation of railway marshalling yards under various parameters of arrivals and different capacity of the yards. It is unlikely to completely replace the conventional methods of researching the operation of railway stations based on detailed descriptions. However, the study shows that it is quite usable as a primary analysis tool that does not require significant efforts and detailed statistics.
Keywords
About the Authors
M. L. ZharkovRussian Federation
Maxim L. Zharkov, Candidate of Engineering, Researcher
Shelekhov, Irkutsk Oblast
M. M. Pavidis
Russian Federation
Mikhail M. Pavidis, post-graduate student
Irkutsk
References
1. Gnedenko B.V., Kovalenko B.V. [Introduction into the queueing theory]. Moscow: LKI; 2007. (in Russ.)
2. Akulinichev V.M., Kudriavtsev V.A., Koreshkov A.N. [Mathematical methods in the operation of railways]. Moscow: Transport; 1981.
3. Kazakov A.L., Maslov A.M. [Construction of a model of an uneven traffic flow. Case study of a railway freight station]. Modern Technologies. Systems Analysis. Modeling 2009;3:27-32. (in Russ.)
4. Zhuravskaya M.A., Kazakov A.L., Zharkov M.L. et al. Simulating the operation of transport hub of a metropolis as a three-phase queueing system. Transport of the Urals 2015;3:17-22. (in Russ.)
5. Weik N., Niebel N. Capacity analysis of railway lines in Germany – a rigorous discussion of the queueing based approach. Journal of Rail Transport Planning & Management 2016;6(2):99-115.
6. Dorda M., Teichmann D. Modelling of freight trains classification using queueing system subject to breakdowns. Mathematical Problems in Engineering 2013;2013:11.
7. Nießen N. Waiting and loss probabilities for route nodes. In: Proceedings of the 5th International Seminar on Railway Operations Modelling and Analysis. Copenhagen (Denmark); 2013.
8. Bronshtein O.I., Raykin A.A., Rykov V.V. [On a queueing system with losses]. Izvestiia Akademii nauk SSSR: Tekhnicheskaia kibernetika 1964;4:39-47. (in Russ.)
9. Kitaev M.Yu., Rykov V.V. Controlled queueing system. N.Y.: CC Press; 1995.
10. Rykov V.V. Controllable queueing systems from the very beginning up to nowadays. Materials of Information Technologies and Mathematical Modelling named after A.F. Terpugov 2017;1:25-26.
11. Grachev V.V., Moiseev A.N., Nazarov A.A. et al. Multistage queueing model of the distributed data processing system. Proceedings of TUSUR University 2012;2- 2(26):248-251. (in Russ.)
12. Shklennik M., Moiseeva S., Moiseev A. Optimization of two-level discount values using Queueing tandem model with feedback. Communications in Computer and Information Science 2018;912:321-332.
13. Galileyskaya A.A., Lisovskaya E.Yu., Moiseeva S.P. et al. On sequential data processing model that implements the backup storage. Modern Information Technologies and IT-Education 2019;3:579-587. (in Russ.)
14. Dudin A.N., Klimenok V.I. [Queueing systems with correlated flows]. Minsk: BSU; 2000. (in Russ.)
15. Kim C., Dudin A., Dudina O. et al. Tandem queueing system with infinite and finite intermediate buffers and generalized phase-type service time distribution. European Journal of Operational Research 2014;23:170-179.
16. Vishnevskii V.M., Dudin A.N. Queueing systems with correlated arrival flows and their applications to modeling telecommunication networks. Automation and remote control 2017;8:3-59. (in Russ.)
17. Gasnikov A.V., editor. [Introduction into the mathematical modelling of traffic flows]. Moscow: MIPT; 2010. (in Russ.)
18. Lucantoni D.M. New results on single server queue with a batch Markovian arrival process. Commun. Statist. Stochastic Models 1991;7:1-46.
19. Kazakov A.L., Pavidis M., Zharkov M.L. Multiphase systems of mass service in switchyard modeling. Herald of the Ural State University of Railway Transport 2018;2:4-14.
20. Kazakov A.L., Pavidis M. On one approach to model operation of marshalling stations. Transport of the Urals 2019;1(60):29-35. (in Russ.)
21. Zharkov M.L., Pavidis M.M. Modelling of railway stations based on queueing networks. Aktualnye Problemy Nauki Pribaikalia 2020;3:79-84. (in Russ.)
22. Walrand J. An introduction to queueing networks. Mir; 1993.
23. Ivnitsky V.A. Queueing network theory. Moscow: FIZMATLIT; 2004. (in Russ.)
24. Kelton D.W, Law A.M. Simulation modelling and analysis. Saint Petersburg: Piter; 2004.
25. Zharkov M.L., Parsyurova P.A., Kazakov A.L. Modeling operation of railway stations and rail network sections based on studying train schedule deviations. Proceedings of Irkutsk State Technical University 2014;6(89):23-31. (in Russ.)
26. Bychkov I.V., Kazakov A.L., Lempert A.A. et al. [An intelligent system for managing the transportation and logistics infrastructure of a region]. Control Sciences 2014;1:27-35. (in Russ.)
Review
For citations:
Zharkov M.L., Pavidis M.M. Simulation of railway marshalling yards using the methods of the queueing theory. Dependability. 2021;21(3):27-34. https://doi.org/10.21683/1729-2646-2021-21-3-27-34