MATHEMATICAL DESCRIPTION AND MODELLING OF TRANSPORTATION OF CARGOES ON THE BASE DIGITAL RAILWAY
DOI:
https://doi.org/10.17770/etr2019vol2.4049Keywords:
asymmetric analytics, digitization of transportation, cost of speed, time, dynamic model, Smart DateAbstract
This article presents the results of a mathematical description of the transportation process of goods by rail on the exit routes. The parameters reflecting state of time, speed and cost of the actual performance of the freight transportation are simulated, which makes it possible to identify and respond in time to the risk caused by interaction between adjacent subjects and objects for transportation. An algorithm to respond to a decrease in speed of transportation is determined. It is substantiated that the efficiency in transportation provides the level of development in transport and logistics system as an infrastructure of a new technological order. The price of these systems generates added value due to speed, inter modality services, drawing up optimal routes for cargo delivery, ensuring full car loads, passage control of goods at all stages of the logistics chain, etc., i.e. through the integration of products and services, considering the dominant global network of production and consumption. This work is an implementation element of digital formats in the operational activity of railways. The created model implements the “traceability” of information about the movement of cargo traffic in exit routes, generating Fast Date for time-sensitive decision-making process and Smart Date for asymmetric analytics. In contrast to the traditional model of transportation, the proposed solution is based on a mathematical description of all stages of the life cycle of freight (trains), which allows evaluating all costs by type of each process of transportation (movement and idle time) in real time mode. This approach takes into account the "investment" in the formation of value of all enterprises involved in transportation, including the condition and operation of technical infrastructure, locomotives, locomotive crews, wagon and freight facilities employees, and the movers themselves who provide and manage the transportation process. It is proved that the further growth of the profitability of the transport business is in direct correlation with the increase in the marginal profitability of shippers, and decrease of the transport component in the final price of goods achieved as result of digitizing the process of cargo transportation in the exit routes. The research methodology is based on the process-functional approach to describing the life cycle of a freight train, analysis factor for technical and economic characteristics of the transportation process and dynamic modelling of the parameters of significant means of elements affecting the transportation process. The information basis of the study relies on a representative sample of loading and unloading of goods in areas of mass traffic. We have investigated dependent (homomorphic) and independent (singular) pairs in accordance with the time, cost, and technical parameters.Downloads
References
O. Álvarez-SanJaime, P. Cantos-Sanchez, R. Moner-Colonques, J.J. Sempere-Monerris “A model of internal and external competition in a High Speed Rail line” Economics of Transportation, 2015, vol. 4, Iss. 3, pp. 178-187.
R. Vickerman “Can high-speed rail have a transformative effect on the economy?” Transport Policy, 2018, vol. 62, pp. 31-37.
V. Allee The Future of Knowledge. USA: Butterworth-Heinemann, 2002.
C. Parolloni The Value Net: A Tool for Competitive Strategy. New York: Villey, 1999.
M.W. Jonson, M.C. Christensen, H. Kadermann “Reinventing your business model”, Harvard Business Review, December 2008 [Online]. Available: https://hbr.org/2008/12/reinventing-your-business-model. [Accessed: Jan. 10, 2019].
P. Vilela, M. Cachoni, A. Vieira, L. Christofoletti, Train circulation planning: Quantitative approaches, Conference: 2017 Joint Rail Conference. Philadelphia, Pennsylvania, USA, April 4–7, 2017. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-5026810165&doi=10.1115%2fJRC2017-2223&partnerID=40&md5=5badb9c5064e86e8b7f16f70e8667a16. [Accessed: Nov. 10, 2017].
I.V. Bychkov, A.L. Kazakov, A.A. Lempert, D.S. Bukharov, A.B. Stolbov “An intelligent management system for the development of a regional transport logistics infrastructure”, Automation and Remote Control, 2016, vol. 77, Iss. 2, pp. 332-343.
Y. Ma, D. Chang, F. Wang “Integrating business processes of container sea-rail combined transport”, International Journal of Internet Manufacturing and Services, 2019, vol. 6, No 1, pp. 48-63.
L. Heilig, S. Voß, “Information systems in seaports: a categorization and overview”, Information Technology and Management, 2017, vol. 18, Iss. 3, pp. 179-201.
P. Fraga-Lamas, T.M. Fernández-Caramés, L. Castedo “Towards the internet of smart trains: a review on industrial IoT-connected railways”, Sensors (Basel), 2017, vol. 17, no. 6, no. 1457, pp. 1-44.
R. Song, X. Xue, “Freight volume forecast based on improved radial basis function neural network”, Boletin Tecnico, 2017, vol. 55, iss. 5, pp. 419-423.
L. Azpilicueta, J.J. Astrain, P. Lopez-Iturri, F. Granda, C. Vargas-Rosales, J. Villadangos, A. Perallos, A. Bahillo, F. Falcone “Optimization and design of wireless systems for the implementation of context aware scenarios in railway passenger vehicles”, IEEE Transactions on Intelligent Transportation Systems, 2017, vol. 18, iss. 10, pp. 2838-2850.
C.O. Cruz, J.M. Sarmento “Maximizing the value for money of road projects through digitalization”, Competition and Regulation in Network Industries, November 27, 2018. [Online]. Available: https://doi.org/10.1177/1783591718811436 [Accessed: Jan. 18, 2019].
M. Schneider, N. Nießen “Minimising economic losses due to inefficient rescheduling”, Journal of Rail Transport Planning & Management, 2016, vol. 6, iss. 2, pp. 128-140.
A. Nagurney “Mathematical models of transportation and networks (Refereed Encyclopedia Article)” Mathematical Models in Economics, Encyclopedia of Life Support Systems, 2007 [Online]. Available: http://works.bepress.com/anna_nagurney/191/ [Accessed: Jan. 15, 2019].
L. R. Ford, D. R. Fulkerson "Maximal flow through a network", Canadian Journal of Mathematics, 1956, vol. 8, pp. 399–404.
J. R. Evans, E. Minieka Optimization algorithms for networks and graphs. 2nd ed. New York: Marcel Dekker, 1992.