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Improving the reliability of trucking in the conditions of a mining enterprise

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Category: Content №3 2022

Last Updated on 29 June 2022

Published on 30 November -0001

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Authors:

G.Kairatkyzy, orcid.org/0000-0002-5023-9787, Academy of Logistics and Transport, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Y.Y.Karsybayev, orcid.org/0000-0001-7942-716X, Civil Aviation Academy, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.Z.Abzhapbarova, orcid.org/0000-0001-7013-0909, Civil Aviation Academy, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.V.Deryugin, orcid.org/0000-0002-2456-7664, Dnipro University of Technology, Dnipro, Ukraine, email: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.K.Bas, orcid.org/0000-0003-0496-4379, Dnipro University of Technology, Dnipro, Ukraine, email: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (3): 125 - 130

https://doi.org/10.33271/nvngu/2022-3/125

Abstract:

Purpose. Improving the efficiency of trucking (TR) in the conditions of a mining enterprise by means of developing recommendations aimed at enhancing reliability of the transport process.

Methodology. To determine the factors influencing the reliability of TR the Functional Resonance Analysis Method (hereinafter FRAM) was used, which is based on the study on the functions of freight automobile transportation process with respect to six different aspects: time, control, output, resource, prerequisites, and entrance.

Findings. The transport process of TR in the conditions of a mining enterprise is represented by five main functions: preparation of TR, supply of the truck for loading, loading of cargo, transportation and unloading of cargo at the destination point. For each function of the transport process TR we determined its variability as based on the accuracy and timeliness of the transport operation; identified factors that affect the reliability of the transport process, namely driver experience, administrative control, time of the transport operation, complexity of the transport operation, workplace ergonomics, workload and stress, the level of management support that may worsen the final result of goods delivery to the point of unloading. It is determined that preparation and transportation of cargo to the destination point is the least reliable function of TR transport process. This is due to the significant changeability and variability, a large number of production tasks and high variable standards of transport work. It is proposed to strengthen the control over the psychophysiological condition of the driver in order to improve the reliability of TR in the conditions of a mining enterprise and to reduce the probability of failures during the performance of transportation work.

Originality. It consists in establishing the relationship between the functions and factors of the transport process of TR in the conditions of a mining enterprise, which allows assessment of the reliability level of the task in a timely manner.

Practical value. It consists in a quantitative assessment of the impact of transportation process factors on the reliability of TR in the conditions of a mining enterprise.

Keywords: reliability, transportation process, driver, psychophysiological condition, FRAM method, safety

References.

1. Sadkowski, 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.

2. Naumov, V., Taran, I., Litvinova, Z., & Bauer, M. (2020). Optimizing resources of multimodal transport terminal for material flow service. Sustainability (Switzerland), 12(16), 6545. https://doi.org/10.3390/su12166545.

3. Sabraliev, N., Abzhapbarova, A., Nugymanova, G., Taran, I., & Zhanbirov, Z. (2019). Modern aspects of modeling of transport routes in Kazakhstan. News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciencesthis, 2(434), 62-68. https://doi.org/10.32014/2019.2518-170X.39.

4. World Health Organization (2021). Road traffic injuries. Retrieved from https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries#.

5. Official website of the European Union (n.d.). Road safety: 4 000 fewer people lost their lives on EU roads in 2020 as death rate falls to all time low. Retrieved from https://ec.europa.eu/transport/modes/road/news/2021-04-20-road-safety_en.

6. Site of the patrol police of Ukraine. Statistics (n.d.). Statistics of road accidents in Ukraine for the period from 01.01.2020 to 31.12.2020. Retrieved from http://patrol.police.gov.ua/statystyka/.

7. Golinko, V., Cheberyachko, S., Deryugin, O., Tretyak, O., & Dusmatova, O. (2020). Assessment of the Risks of Occupational Diseases of the Passenger Bus Drivers. Safety and Health at Work, 11(4), 543-549. https://doi.org/10.1016/j.shaw.2020.07.005.

8. Borodina, N., Cheberiachko, S., Deryugin, ., Tretyak, O., & Bas,I. (2021). Occupational risk assessment of passenger bus drivers. Journal of Scientific Papers Social Development and Security, 11(2), 81-90. https://doi.org/10.33445/sds.2021.11.2.8.

9. Trojanowski, P., & Trojanowska, J. (2021). Reliability of Road Transport Means as a Factor Affecting the Risk of Failure The Transport Problem Case Study. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Zajac, J., & Perakovi, D. (2021). Advances in Design, Simulation and Manufacturing IV. DSMIE 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-77719-7_26.

10. Cedillo-Campos, M.G., Prez-Gonzlez, C.M., Pia-Barcena,J., & Moreno-Quintero, E. (2019). Measurement of travel time reliability of road transportation using GPS data: A freight fluidity approach. Transportation Research Part A: Policy and Practice, (130), 240-288. https://doi.org/10.1016/j.tra.2019.09.018.

11. Juhsz, M., Mtrai, T., & Koren, C. (2017). Forecasting travel time reliability in urban road transport. Archives of Transport, 43(3), 53-67. https://doi.org/10.5604/01.3001.0010.4227.

12. De Jong, G.C., & Bliemer, M.C.J. (2015). On including travel time reliability of road traffic in appraisal. Transportation Research Part A: Policy and Practice, 73(C), 80-95. https://doi.org/10.1016/j.tra.2015.01.006.

13. Taran, I., & Litvin, V. (2018). Determination of rational parameters for urban bus route with combined operating mode. Transport Problems, 13(4), 157-171. https://doi.org/10.20858/tp.2018.13.4.14.

14. Bjrnsen, K., Jensen, A., & Aven, T. (2018). Using qualitative types of risk assessments in conjunction with FRAM to strengthen the resilience of systems. Journal of Risk Research, 23(13), 1-14. https://doi.org/10.1080/13669877.2018.1517382.

15. Hussein, S., & Nadeau, S. (2019). Proposal for a Predictive Performance Assessment Model in Complex Sociotechnical Systems Combining Fuzzy Logic and the Functional Resonance Analysis Method (FRAM). American Journal of Industrial and Business Management, 9(6), 1345-1375. https://doi.org/10.4236/ajibm.2019.96089.

16. Patriarca, R., DiGravio, G., & Costantino, F. (2017). A Monte Carlo evolution of the Functional Resonance Analysis Method (FRAM) to assess performance variability in complex systems. Safety Science, 91, 49-60. https://doi.org/10.1016/j.ssci.2016.07.016.

17. Hollnagel, E. (2012). FRAM, the Functional Resonance Analysis Method: Modeling Complex Socio-Technical Systems. Ashgate Publishing, Ltd., Farnham. 160 p. ISBN-13: 978-1409445517.

18. Salihoglu, E., & Beiki, E.B. (2021). The use of Functional Resonance Analysis Method (FRAM) in a maritime accident: A case study of Prestige. Ocean Engineering, 219, 108223. https://doi.org/10.1016/j.oceaneng.2020.108223.

19. Clay-Williams, R., Hounsgaard, J., & Hollnagel, E. (2015). Where the rubber meets the road: using FRAM to align work-as-imagined with work-as-done when implementing clinical guidelines. Implementation Science, 10, 125. https://doi.org/10.1186/s13012-015-0317-y.

20. Riccardo, P., Di Gravio, G., & Costantino, F. (2017). A Monte Carlo evolution of the Functional Resonance Analysis Method (FRAM) to assess performance variability in complex systems. Safety Science, 91, 49-60. https://doi.org/10.1016/j.ssci.2016.07.016.

21. Patriarca, R., Bergstrm, J., & Di Gravio, G. (2017). Defining the Functional Resonance Analysis space: combining Abstraction Hierarchy and FRAM. Reliability Engineering & System Safety, 165, 34-46. https://doi.org/10.1016/j.ress.2017.03.032.

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