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Determining the efficient management system for a specialized transport enterprise

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Category: Contens №4 2020

Last Updated on 05 September 2020

Published on 30 August 2020

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Authors:

V. Volkov, orcid.org/0000-0003-2202-3441, Kharkiv National Automobile and Highway University, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I. Taran, orcid.org/0000-0002-3679-2519, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

T. Volkova, orcid.org/0000-0001-8546-4119, Kharkiv National Automobile and Highway University, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O. Pavlenko, orcid.org/0000-0003-4237-4310, Kharkiv National Automobile and Highway University, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

N. Berezhnaja, orcid.org/0000-0001-8740-3387, Kharkiv Petro Vasylenko National Technical University of Agriculture, Kharkiv, Ukraine, е-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2020, (4): 185-191

https://doi.org/10.33271/nvngu/2020-4/185

повний текст / full article

Abstract:

Purpose. To identify the efficient management system for a specialized transport enterprise in terms of the varied demand owing to the parameter optimization and cost reduction.

Methodology. An approach to identify management system of the specialized transport enterprise has been proposed relying upon the system analysis principles and upon the apparatus of queuing theory. Construction of the regression models involved formalization of expenditures connected with order servicing, while a level of the parameter system effect on the estimated figure was determined using regression analysis.

Findings. Analysis of the theoretical evidence has shown that despite the numerous developed approaches intended to improve management systems for enterprises of different branches, which propose modern methods and models, it is required to develop absolutely cost-effective and adaptive procedure to determine the efficient management system for a specialized transport enterprise. An approach determining such an efficient management system exemplified by a business unit of Illich Iron & Steel Works PJSC (Mariupol) has been proposed. Its implementation anticipates two levels: in terms of service parameters, and in terms of expenditures connected with order service of the enterprise. For the purpose, a mathematical model of such an order service system has been developed. The model takes into consideration various probability factors (i.e. ordering moments, service period, and others). Moreover, service costs have been formalized to identify optimum conditions. Values of the latter and influence parameters have been applied to develop regression models in the power form with a nonzero coefficient. The models will help identify online optimum service conditions and make managerial decisions concerning variation of the number of repair crews, vehicles, and so on.

Originality. For the first time, the paper proposes an approach as for the determination of the efficient management system for a specialized transport enterprise based upon a queuing theory taking into consideration a system analysis in the interaction between production enterprises and transport ones. Moreover, forecast models are developed concerning expenditures connected with order service of mining and metallurgical companies depending upon the number of crews, service time variations and the order quantities.

Practical value. The approach is the theoretical background to improve interaction between the specialized transport enterprises, and mining and metallurgical companies. The models may be used to develop efficient management system relying upon determination of ratio number of repair crews and vehicles at a motor transport enterprise; in turn, that will help to reduce expenditures connected with the resource use.

References.

1. Kozhevnikova, N. Yu. (2013). Specialized rolling stock as a priority growth area of a motor transport enterprise. Agrarnoe obrazovanie i nauka, (4), 10-14.

2. Naumov, V. (2017). Estimating the Vehicles’ Number for Servicing a Flow of Requests on Goods Delivery. Transportation Research Procedia, 27, 412-419. https://doi.org/10.1016/j.trpro.2017.12.063.

3. Rossolov, A., Popova, N., Kopytkov, D., Rossolova, H., & Za­porozhtseva, H. (2018). Assessing the impact of parameters for the last mile logistics system on creation of the added value of goods. Eastern-European Journal of Enterprise Technologies, 95, 70-75. https://doi.org/10.15587/1729-4061.2018.142523.

4. Vojtov, V., Berezchnaja, N., Kravcov, A., & Volkova, T. (2018). Evaluation of the Reliability of Transport Service of Logistics Chains. International Journal of Engineering & Technology, 7(4.3), 270-274. https://doi.org/10.14419/ijet.v7i4.3.19802.

5. Deryugin, O., & Cheberyachko, S. (2015). Substatiation of truck selection in terms of psychophysiologic stress on a driver minimizing. Eastern-European journal of enterprise technologies, 3(75), 15-22. https://doi.org/10.15587/1729-4061.2015.42127.

6. Naumov, V. S., & Kholeva, O. G. (2017). Forming the strategies of sustainable development of freight forwarders at transportation market. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (3), 129-134.

7. Ebrahimi, M., Baboli, A., & Rother, E. (2019.) The evolution of world class manufacturing toward Industry 4.0: A case study in the automotive industry. IFAC-PapersOnLine, 52(10), 188-194. https://doi.org/10.1016/j.ifacol.2019.10.021.

8. Kozachenko, D., Skalozub, V., Gera, B., Hermaniuk, Yu., Korobiova, R., & Gorbova, A. (2019). A model of transit freight distribution on a railway network. Transport Problems, 14(3), 17-26. https://doi.org/10.20858/tp.2019.14.3.2.

9. Gou, J., Li, N., Lyu, T., Lyu, X., & Zhang, Z. (2019). Barriers of knowledge transfer and mitigating strategies in collaborative management system implementations. Journal of Information and Knowledge Management Systems, 49(1), 2-20. https://doi.org/10.1108/VJIKMS-09-2018-0072.

10. Malucelli, F., & Tresoldi, E. (2019). Delay and disruption management in local public transportation via real-time vehicle and crew re-scheduling: a case study. PUBLIC TRANSPORT, 11(1), 1-25. https://doi.org/10.1007/s12469-019-00196-y.

11. See, B. P., Yap, C. S., & Ahmad, R. (2019). Antecedents of continued use and extended use of enterprise systems. Behaviour & Information Technology, 38(4), 384-400. https://doi.org/10.1080/0144929X.2018.1536165.

12. Saraeian, S., Shirazi, B., & Motameni, H. (2019). Adaptive control of criticality infrastructure in automatic closed-loop supply chain considering uncertainty. International Journal of Critical Infrastructure Protection, 25, 102-124. https://doi.org/10.1016/j.ijcip.2019.02.004.

13. Shramenko, N., Pavlenko, O., & Muzylyov, D. (2020). Logistics Optimization of Agricultural Products Supply to the European Union Based on Modeling by Petri Nets. In: I. Karabegović (Ed.). New Technologies, Development and Application III. NT 2020. Lecture Notes in Networks and Systems, 128, (pp. 596-604). Cham: Springer. https://doi.org/0.1007/978-3-030-46817-0_69.

14. Gritsuk, I., Volkov, V., Mateichyk, V., Gutarevych, Y., Tsiu­man, M., & Goridko, N. (2017). The Evaluation of Vehicle Fuel Consumption and Harmful Emission Using the Heating System in a Driving Cycle. SAE International Journal of Fuels and Lubricants, 10(1), 236-248. https://doi.org/10.4271/2017-26-0364.

15. Sładkowski, A., Utegenova, A., Kolga, A. D., Gavrishev, S. E., Stolpovskikh, I., & Taran, I. (2019). Improving the efficiency of using dump trucks under conditions of career at open mining works. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (2), 36-42. https://doi.org/10.29202/nvngu/2019-2/8.

16. Turpak, S. M., Taran, I. O., Fomin, O. V., & Tretiak, O. O. (2018). Logistic technology to deliver raw material for metallurgical production. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (1), 162-169. https://doi.org/10.29202/nvngu/2018-1/3.

17. Rossolov, A., Kopytkov, D., Kush, Y., & Zadorozhna, V. (2017). Research of effectiveness of unimodal and multimodal transportation involving land modes of transport. Eastern-European Journal of Enterprise Technologies, 5(89), 60-69. https://doi.org/10.15587/1729-4061.2017.112356.

18. Shramenko, N., & Muzylyov, D. (2020). Forecasting of Overloading Volumes in Transport Systems Based on the Fuzzy-Neural Model. Advances in Design, Simulation and Manufacturing II. DSMIE 2019. Lecture Notes in Mechanical Engineering, (pp. 311-320). Cham: Springer. https://doi.org/10.1007/978-3-030-22365-6_31.

19. Naumov, V., Nagornyi, I., & Litvinova, Y. (2015). Model of multimodal transport node functioning. Archives of Transport, 36(4), 43-54. https://doi.org/10.5604/08669546.1185202.

20. Ni, D. (2015). Traffic Flow Theory. Butterworth-Heinemann. Retrieved from https://www.elsevier.com/books/traffic-flow-theory/ni/978-0-12-804134-5.

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