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Improving the efficiency of management of transport and energy resources of the logistics system of an industrial enterprise

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Category: Content №6 2024

Last Updated on 28 December 2024

Published on 30 November -0001

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Authors:

S.Turpak, orcid.org/0000-0003-3200-8448, National University Zaporizhzhia Polytechnic, Zaporizhzhia, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

N.Saukhanov*, orcid.org/0009-0004-7292-4752, Aktobe Regional University named after K. Zhubanov, Aktobe, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.Ostrohliad, orcid.org/0000-0002-8496-3271, National University Zaporizhzhia Polytechnic, Zaporizhzhia, 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.

D.Moroz, orcid.org/0000-0003-2577-3352, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (6): 143 - 150

https://doi.org/10.33271/nvngu/2024-6/143

Abstract:

Purpose. To define peculiarities of the processes and indicators of the functioning of the transport and warehouse logistics system (TWLS) of industrial enterprises using a simulation model that takes into account the need to implement a cargo delivery plan to warehouses in conditions of restoring the flowability of cargo during unloading, in order to determine the optimal operating parameters based on the criterion of minimum costs.

Methodology. To build a model of the functioning of the transport-warehouse logistics system of unloading raw materials at the enterprise, the method of simulation modelling was used, statistical analysis was used to establish the laws of distribution of the input parameters of the model. The assessment of the use of energy resources was carried out using a regression analysis of the duration of heating of various types of cargo at industrial enterprises.

Findings. The conducted experiments on the developed simulation model made it possible to determine the rational number of used resources – locomotives and cargo heating chambers when they freeze. Due to the use of the proposed methods and model, the efficiency of the management of transport and energy resources of the logistics system of an industrial enterprise has been increased.

Originality. The developed simulation model of the functioning of the raw material and fuel unloading subsystem, unlike the existing ones, takes into account the peculiarities of the transport and storage process in the cold period of the year and allows determining the rational number of transport and auxiliary resources, taking into account the influence of external factors.

Practical value. The obtained results make it possible to increase the efficiency of the TWLS operation of industrial enterprises due to the development of methods based on the logistic approach to the management of transport-warehouse processes. The simulation model of the management of the heating and unloading process allows you to determine the most economical mode of operation of transport and warehouse logistics systems of industrial enterprises by determining the rational number of involved resources.

Keywords: simulation modelling, railway freight transport, transport logistics; wagon, resource management, energy consumption

References.

1. Ézsiás, L., Tompa, R., & Fischer, S. (2024). Investigation of the possible correlations between specific characteristics of crushed stone aggregates. Spectrum of Mechanical Engineering and Operational Research, 1(1), 10-26. https://doi.org/10.31181/smeor1120242.

2. Fischer, S. (2021). Investigation of effect of water content on railway granular supplementary layers. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (3), 64-68. https://doi.org/10.33271/nvngu/2021-3/064.

3.  Eller, B., Majid, M. R., & Fischer, S. (2022). Laboratory tests and FE modeling of the concrete canvas, for infrastructure applications. Acta Polytechnica Hungarica, 19(3), 9-20. https://doi.org/10.12700/APH.19.3.2022.3.2.

4. Bazaluk, O., Ashcheulova, O., Mamaikin, O., Khorolskyi, A., Lozynskyi, V., & Saik, P. (2022). Innovative activities in the sphere of mining process management. Frontiers in Environmental Science, (10), 878977. https://doi.org/10.3389/fenvs.2022.878977. 

5. Salieiev, I. (2024). Organization of processes for complex mining and processing of mineral raw materials from coal mines in the context of the concept of sustainable development. Mining of Mineral Deposits, 18(1), 54-66. https://doi.org/10.33271/mining18.01.054.

6. Naumov, V., Bekmagambetova, L., Bitileuova, Z., Zhanbirov, Z., & Taran, I. (2022). Mixed Fuzzy-Logic and Game-Theoretical Approach to Justify Vehicle Models for Servicing the Public Bus Line. Communications – Scientific Letters of the University of Zilina, 24(1), A26-A34. https://doi.org/10.26552/com.c.2022.1.a26-a34.

7. Volkov, V., Taran, I., Volkova, T., Pavlenko, O., & Berezhnaja, N. (2020). Determining the efficient management system for a specialized transport enterprise. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (4), 185-191. https://doi.org/10.33271/nvngu/2020-4/185.

8. Saukenova, I., Oliskevych, M., Taran, I., Toktamyssova, A., Aliakbarkyzy, D., & Pelo, R. (2022). Optimization of schedules for early garbage collection and disposal in the megapolis. Eastern-European Journal of Enterprise Technologies, 1(3(115)), 13-23. https://doi.org/10.15587/1729-4061.2022.251082.

9. Taran, I., Karsybayeva, A., Naumov, V., Murzabekova, K., & Cha­zha­bayeva, M. (2023). Fuzzy-Logic Approach to Estimating the Fleet Efficiency of a Road Transport Company: A Case Study of Agricultural Products Deliveries in Kazakhstan. Sustainability, 15(5), 4179. https://doi.org/10.3390/su15054179.

10. Koval, V., Kryshtal, H., Udovychenko, V., Soloviova, O., Froter, O., Kokorina, V., & Veretin, L. (2023). Review of mineral resource management in a circular economy infrastructure. Mining of Mineral Deposits, 17(2), 61-70. https://doi.org/10.33271/mining17.02.061. 

11. Sadeghi, M., Bagheri, M., & Pishvaee, M. S. (2021). Evaluation of rail terminals in container ports using simulation: a case study. Simulation, 97(12), 809-820. https://doi.org/10.1177/00375497211024731.

12. Matsiuk, V., Galan, O., Prokhorchenko, A., & Tverdomed, V. (2021). An Agent-Based Simulation for Optimizing the Parameters of a Railway Transport System. ICTERI-2021, 121-128.

13. Mazaraki, A., Matsiuk, N., Ilchenko, V., Kavun-Moshkovska, O., & Grigorenko, T. (2020). Development of a multimodal (railroad-water) chain of grain supply by the agent-based simulation method. Eastern-European Journal of Enterprise Technologies, 6/3(108), 14-22.

https://doi.org/10.15587/1729-4061.2020.220214.

14. Yan, B., Zhu, X., Lee, D. H., Jin, J. G., & Wang, L. (2020). Transshipment operations optimization of sea-rail intermodal container in seaport rail terminals. Computers & Industrial Engineering, 141, 106296. https://doi.org/10.1016/j.cie.2020.106296.

15. Namazov, N., Matsiuk, V., Bulgakova, Iu., Nikolaienko, I., & Vernyhora, R. (2023). Agent-based simulation model of multimodal iron ore concentrate transportation. Machinery & Energetics, 14(1), 46-56. https://doi.org/10.31548/machinery/1.2023.46.

16. Muzylyov, D., & Shramenko, N. (2020). Blockchain Technology in Transportation as a Part of the Efficiency in Industry 4.0 Strategy. Advanced Manufacturing Processes, 216-225. https://doi.org/10.1007/978-3-030-40724-7_22.

17. Michal, G., Huynh, N., Shukla, N., Munoz, A., & Barthelemy, J. (2017). RailNet: A simulation model for operational planning of rail freight. Transportation Research Procedia, 25, 461-473. https://doi.org/10.1016/j.trpro.2017.05.426.

18. Butko, T., Prodashchuk, S., Bogomazova, G., Shelekhan, G., Prodashchuk, M., & Purii, R. (2017). Improvement of technology for management of freight rolling stock on railway transport. Eastern-European Journal of Enterprise Technologies, 3/3(87), 4-11. https://doi.org/10.15587/1729-4061.2017.99185.

19. Silva, J., Ávila, P., Patrício, L., Sá, J. C., Ferreira, L. P., Bastos, J., & Castro, H. (2022). Improvement of planning and time control in the project management of a metalworking industry-case study. Procedia Computer Science, 196, 288-295. https://doi.org/10.1016/j.procs.2021.12.016.

20. Kozachenko, D., Hnennyi, J., Berezovyi, N., & Malashkin, V. (2021). Optimization of the Enterprise Railcar Fleet Structure for the Transportation of Iron Ore Raw Materials. In Transport Means-Proceedings of the International Conference, 316-321.

21. Ricci, S., Capodilupo, L., & Tombesi, E. (2016). Discrete events simulation of intermodal terminals operation: modelling techniques and achievable results. Civil-Comp Proceedings, 110.  https://doi.org/10.4203/ccp.110.288.

22. Yang, Y., Zhou, Q., & Chen, K. (2022). Multiagent-Based Modeling and Simulation of a Coal Multimodal Transport System. IEEE Access, 10, 65873-65885. https://doi.org/10.1109/ACCESS.2022.3184728.

23. Muzylyov, D., Shramenko, N., & Ivanov, V. (2021). Management decision-making for logistics systems using a fuzzy-neural simulation. In Advances in Industrial Internet of Things, Engineering and Management, 175-192. https://doi.org/10.1007/978-3-030-69705-1_11.

24. Yaagoubi, E. l., Ferjani, A., Essaghir, Ya., Sheikhahmadi, F., Abourraja, M. N., Boukachour, J., ..., & Khodadad-Saryazdi, A. (2022). A logistic model for a French intermodal rail/road freight transportation system. Transportation Research Part E: Logistics and Transportation Review, 164, 102819. https://doi.org/10.1016/j.tre.2022.102819.

25. Usmonov, J., & Djuraev, T. (2021). Imitation models of the railway organization for railway transport flows. Technical science and innovation, 4, 196-202. https://doi.org/10.51346/tstu-01.21.4-77-0146.

26. Bal, F., & Vleugel, J. (2021). Inland rail freight services with less fuel and lower emissions. International Journal of Energy Production and Management, 6(2), 170-180. https://doi.org/10.2495/EQ-V6-N2-170-180.

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