Improvement of technology for management of freight rolling stock on railway transport
DOI:
https://doi.org/10.15587/1729-4061.2017.99185Keywords:
railway transport, artificial neural networks, irregularity factor, management of transportationAbstract
We performed a statistical analysis of the time series of the volumes of cargo transportation. Studies have shown sufficiently high accuracy of prediction relative to the actual values of a railway transport system based on the mathematical apparatus of artificial neural network. The experiment revealed that the mean absolute percentage error for the volumes of transportation of grain and the products of flour mills amounted to 5.56 %. Given that railway transport is a fairly inert system, indicator of 5.56 % is sufficient for management decision making. By having predicted the level of cargo transportation, we determined the required number of wagons of a particular type, which would conform to the conditions of transportation of this particular cargo.
The optimal technology of organization of railway wagon flows implies minimization of operational costs for the transportation of cargo. In order to find the best variant to move the wagons, we proposed to take into account irregularity factor, or seasonality. The application on the railway network of the result of solution of the proposed model enables the dispatcher, the one who handles wagons, to make rational management decisions. Such technology makes it possible to take both long-term and operational decisions directly in the system of organization of railway wagon flows.
To automate management decision-making by operational personnel of railway transport, we simulated organization of wagon flows using the software. The simulation was carried out on a virtual polygon of railways. The procedure for obtaining rational decisions when managing freight rolling stock is universal and makes it possible to perform calculations for polygons of any size and at arbitrary time of planningReferences
- Vantazhni perevezennia u 2016 rotsi. Available at: http://www.ukrstat.gov.ua/operativ/operativ2016/tr/vp/vp_u/vp1216_u.htm
- Prodashchuk, S. M., Bogomazova, G. Ye., Purii, R. A. (2016). Nova kontseptsiia taryfnoi polityky dlia vnutrishnikh zaliznychnykh vantazhnykh perevezen. Zbirnyk naukovykh prats Ukrainskoho derzhavnoho universytetu zaliznychnoho transportu, 164, 161–169.
- Chuchueva, I. A. (2010). Prognozirovanie vremennyih ryadov pri pomoschi modeli ekstrapolyatsii po vyiborke maksimalnogo podobiya. Nauka i sovremennost, 1-2, 187–192.
- Pradhan, R. P., Kumar, R. (2010). Forecasting Exchange Rate in India: An Application of Artificial Neural Network Model. Journal of Mathematics Research, 2 (4), 111–117. doi: 10.5539/jmr.v2n4p111
- Panchenko, S. V., Butko, T. V., Prohorchenko, A. V., Parhomenko, L. A. (2016). Formirovanie avtomatizirovannoy sistemyi rascheta propusknoy sposobnosti zheleznodorozhnyih setey dlya prodvizheniya gruzopotokov predpriyatiy gorno-metallurgicheskogo kompleksa. Naukoviy visnik natsionalnogo girnichogo universitetu, 2, 93–99.
- Kopytko, V. I., Datskiv, Yu. O. (2011). Prohnozuvannia obsiahiv vantazhnykh perevezen zaliznyts v rehionakh. Naukovyi visnyk NLTU Ukrainy, 21.10, 139–144.
- Gheyas, I., Smith, L. (2009). A Neural Network Approach to Time – Series Forecasting. Proceedings of the World Congress on Engineering, ІІ, 1292–1296.
- Wang, Y., Sun, H., Zhu, J., Zhu, B. (2015). Optimization Model and Algorithm Design for Airline Fleet Planning in a Multiairline Competitive Environment. Mathematical Problems in Engineering, 2015, 1–13. doi: 10.1155/2015/783917
- Najaf, P., Famili, S. (2013). Application of an Intelligent Fuzzy Regression Algorithm in Road Freight Transportation Modelling. Promet – Traffic&Transportation, 25 (4), 311–322. doi: 10.7307/ptt.v25i4.337
- Morariu, N., Iancu, E., Vlad, S. (2009). A Neural Network Model For Time – Series Forecasting. Romanian Journal of Economic Forecasting, 4, 213–223.
- Panchenko, S., Lavrukhin, О., Shapatina, O. (2017). Creating a qualimetric criterion for the generalized level of vehicle. Eastern-European Journal of Enterprise Technologies, 1 (3 (85)), 39–45. doi: 10.15587/1729-4061.2017.92203
- Butko, T., Prokhorov, V., Chekhunov, D. (2017). Devising a method for the automated calculation of train formation plan by employing genetic algorithms. Eastern-European Journal of Enterprise Technologies, 1 (3 (85)), 55–61. doi: 10.15587/1729-4061.2017.93276
- Xie, M.-Q., Li, X.-M., Zhou, W.-L., Fu, Y.-B. (2014). Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks. Computational Intelligence and Neuroscience, 2014, 1–8. doi: 10.1155/2014/375487
- Tortum, A., Yayla, N., Gokdag, M. (2009). The Modelling of Mode Choices of Intercity Freight Transportation with the Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System. Expert Systems with Applications, 36 (3), 6199–6217. doi: 10.1016/j.eswa.2008.07.032
- Borovikov, V. P. (2001). Programma STATISTICA dlya studentov i inzhenerov. Мoscow: Goryachaya liniya – Telekom, 301.
- Borovikov, V. P., Ivchenko, G. I. (2000). Prognozirovanie v sisteme Statistica v srede Windows. Osnovyi teorii i intensivnaya praktika na kompyutere. Мoscow: Finansyi i statistika, 283.
- Vidpravlennya vantazhiv zaliznychnym transportom u sichni-veresni 2016 roku. Available at: http://www.ukrstat.gov.ua/operativ/operativ2016/tr/opr/opr_u/opr0916_u.htm
- Kruglov, V. V., Borisov, V. V. (2002). Iskusstvennyie neyronnyie seti. Teoriya i praktika. Мoscow: Goryachaya liniya – Telekom, 382.
- Haykin, S. (2006). Neyronnyie seti. Мoscow: OOO «I. D. Vilyams», 1104.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Tetiana Butko, Svіtlana Prodashchuk, Ganna Bogomazova, Ganna Shelekhan, Mikola Prodashchuk, Roman Purii
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.
