DOI: https://doi.org/10.15587/1729-4061.2017.99185

Improvement of technology for management of freight rolling stock on railway transport

Tetiana Butko, Svіtlana Prodashchuk, Ganna Bogomazova, Ganna Shelekhan, Mikola Prodashchuk, Roman Purii

Abstract


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 planning

Keywords


railway transport; artificial neural networks; irregularity factor; management of transportation

References


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.


GOST Style Citations


Vantazhni perevezennia u 2016 rotsi [Electronic resource]. – Available at: http://www.ukrstat.gov.ua/operativ/operativ2016/tr/vp/vp_u/vp1216_u.htm

Prodashchuk, S. M. Nova kontseptsiia taryfnoi polityky dlia vnutrishnikh zaliznychnykh vantazhnykh perevezen [Text] / S. M. Prodashchuk, G. Ye. Bogomazova, R. A. Purii // Zbirnyk naukovykh prats Ukrainskoho derzhavnoho universytetu zaliznychnoho transportu. – 2016. – Issue 164. – P. 161–169.

Chuchueva, I. A. Prognozirovanie vremennyih ryadov pri pomoschi modeli ekstrapolyatsii po vyiborke maksimalnogo podobiya [Text] / I. A. Chuchueva // Nauka i sovremennost. – 2010. – Issue 1-2. – P. 187–192.

Pradhan, R. P. Forecasting Exchange Rate in India: An Application of Artificial Neural Network Model [Text] / R. P. Pradhan, R. Kumar // Journal of Mathematics Research. – 2010. – Vol. 2, Issue 4. – P. 111–117. doi: 10.5539/jmr.v2n4p111 

Panchenko, S. V. Formirovanie avtomatizirovannoy sistemyi rascheta propusknoy sposobnosti zheleznodorozhnyih setey dlya prodvizheniya gruzopotokov predpriyatiy gorno-metallurgicheskogo kompleksa [Text] / S. V. Panchenko, T. V. Butko, A. V. Prohorchenko, L. A. Parhomenko // Naukoviy visnik natsionalnogo girnichogo universitetu. – 2016. – Issue 2. – P. 93–99.

Kopytko, V. I. Prohnozuvannia obsiahiv vantazhnykh perevezen zaliznyts v rehionakh [Text] / V. I. Kopytko, Yu. O. Datskiv // Naukovyi visnyk NLTU Ukrainy. – 2011. – Issue 21.10. – P. 139–144.

Gheyas, I. A Neural Network Approach to Time – Series Forecasting [Text] / I. Gheyas, L. Smith // Proceedings of the World Congress on Engineering. – 2009. – Vol. ІІ. – P. 1292–1296.

Wang, Y. Optimization Model and Algorithm Design for Airline Fleet Planning in a Multiairline Competitive Environment [Text] / Y. Wang, H. Sun, J. Zhu, B. Zhu // Mathematical Problems in Engineering. – 2015. – Vol. 2015. – P. 1–13. doi: 10.1155/2015/783917 

Najaf, P. Application of an Intelligent Fuzzy Regression Algorithm in Road Freight Transportation Modelling [Text] / P. Najaf, S. Famili // Promet – Traffic&Transportation. – 2013. – Vol. 25, Issue 4. – P. 311–322. doi: 10.7307/ptt.v25i4.337 

Morariu, N. A Neural Network Model For Time – Series Forecasting [Text] / N. Morariu, E. Iancu, S. Vlad // Romanian Journal of Economic Forecasting. – 2009. – Issue 4. – P. 213–223.

Panchenko, S. Сreating a qualimetric criterion for the generalized level of vehicle [Техt] / S. Panchenko, O. Lavrukhin, O. Shapatina // Eastern-European Journal of Enterprise Technologies. – 2017. – Vol. 1, Issue 3 (85). – P. 39–45. doi: 10.15587/1729-4061.2017.92203 

Butko, T. Devising a method for the automated calculation of train formation plan by employing genetic algorithms [Text] / T. Butko, V. Prokhorov, D. Chekhunov // Eastern-European Journal of Enterprise Technologies. – 2017. – Vol. 1, Issue 3 (85). – P. 55–61. doi: 10.15587/1729-4061.2017.93276 

Xie, M.-Q. Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks [Text] / M.-Q. Xie, X.-M. Li, W.-L. Zhou, Y.-B. Fu // Computational Intelligence and Neuroscience. – 2014. – Vol. 2014. – P. 1–8. doi: 10.1155/2014/375487 

Tortum, A. The Modelling of Mode Choices of Intercity Freight Transportation with the Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System [Text] / A. Tortum, N. Yayla, M. Gokdag // Expert Systems with Applications. – 2009. – Vol. 36, Issue 3. – P. 6199–6217. doi: 10.1016/j.eswa.2008.07.032 

Borovikov, V. P. Programma STATISTICA dlya studentov i inzhenerov [Text] / V. P. Borovikov. – Мoscow: Goryachaya liniya – Telekom, 2001. – 301 p.

Borovikov, V. P. Prognozirovanie v sisteme Statistica v srede Windows. Osnovyi teorii i intensivnaya praktika na kompyutere [Text]: uch. pos. / V. P. Borovikov, G. I. Ivchenko. – Мoscow: Finansyi i statistika, 2000. – 283 p.

Vidpravlennya vantazhiv zaliznychnym transportom u sichni-veresni 2016 roku [Electronic resource]. – Available at: http://www.ukrstat.gov.ua/operativ/operativ2016/tr/opr/opr_u/opr0916_u.htm

Kruglov, V. V. Iskusstvennyie neyronnyie seti. Teoriya i praktika [Text] / V. V. Kruglov, V. V. Borisov. – 2-e izd. – Мoscow: Goryachaya liniya – Telekom, 2002. – 382 p.

Haykin, S. Neyronnyie seti [Text] / S. Haykin. – Мoscow: OOO «I. D. Vilyams», 2006. – 1104 p.







Copyright (c) 2017 Tetiana Butko, Svіtlana Prodashchuk, Ganna Bogomazova, Ganna Shelekhan, Mikola Prodashchuk, Roman Purii

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061