Науковий вісник НГУ

Principles of transport means maintenance optimization: equipment cost calculation

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №5 2023

Last Updated on 27 October 2023

Published on 30 November -0001

Hits: 2303

Authors:

A.Golovan*¹, orcid.org/0000-0001-6589-4381, Odesa National Maritime University, Odesa, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.Gritsuk², orcid.org/0000-0001-7065-6820, Kherson State Maritime Academy, Kherson, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.Honcharuk¹, orcid.org/0000-0002-5306-4206, Odesa National Maritime University, Odesa, 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. 2023, (5): 077 - 084

https://doi.org/10.33271/nvngu/2023-5/077

Abstract:

Purpose. Justification of principles and methodology for effective calculation of the equipment costs and optimization of transport means maintenance.

Methodology. The results of the presented scientific research were obtained using general and special methods of cognition: abstract and logical analysis, systematization and combination, method of theoretical generalization, method of dialectical cognition, deduction and induction, and statistical analysis. This paper analyzes the relationship between the probability of failure prevention by the maintenance system and the associated costs. The research investigates how the variation in the technical condition change rate influences the length of the operation cycle and the rate of its decline. The study’s outcomes are analyzed, including the formation of points of minimum unit costs, the effect of spare parts’ cost, and the practical importance of the conclusions drawn.

Findings. This paper outlines the economic methodology for determining the specific expenses of maintaining means of transport. The methodology considers the distribution of expenses for spare parts, labor, and other components. Using this methodology, it is possible to estimate the total costs of maintenance and make informed decisions about the efficient use of resources. It has been determined that the cost of spare parts impacts the efficiency of the maintenance system. Therefore, it is imperative to balance the cost for spare parts and safety, while considering the probability of failure. The method outlined in this work is versatile, which allows its adaptation and application to the specialized road transport.

Originality. The paper further develops the methodological approach to calculating equipment costs for transport maintenance, which is used to improve service efficiency and reduce expenses. The approach enables a comprehensive evaluation of the outcomes of enhancing failure prevention probability through the maintenance system. It also aids in managing unused parts’ resources, particularly during short operating cycles.

Practical value. The study’s findings can optimize the maintenance system, increase operational efficiency, and enhance the safety and reliability of means of transport, while reducing the costs associated with spare parts, labor, and other maintenance components. This approach aids in conserving resources, reducing operating costs, and is crucial for the financial stability and profitability of management companies.

Keywords: maintenance, ship equipment, costs, failure prevention, operational efficiency

References.

1. Davies, J., Truong-Ba, H., Pardalos, P. M., & Will, G. (2021). Optimal inspections and maintenance planning for anti-corrosion coating failure on ships using non-homogeneous Poisson Processes. Ocean Engineering, 238, 109695. https://doi.org/10.1016/j.oceaneng.2021.109695.

2. Wang, H., Oguz, E., Jeong, B., & Zhou, P. (2018). Life cycle cost and environmental impact analysis of ship hull maintenance strategies for a short route hybrid ferry. Ocean Engineering, 161, 20-28. https://doi.org/10.1016/j.oceaneng.2018.04.084.

3. Tan, Y., Tian, H., Jiang, R., Lin, Y., & Zhang, J. (2020). A comparative investigation of data-driven approaches based on one-class classifiers for condition monitoring of marine machinery system. Ocean Engineering, 201, 107174. https://doi.org/10.1016/j.oceaneng.2020.107174.

4. Lazakis, I., Raptodimos, Y., & Varelas, T. (2018). Predicting ship machinery system condition through analytical reliability tools and artificial neural networks. Ocean Engineering, 152, 404-415. https://doi.org/10.1016/j.oceaneng.2017.11.017.

5. Tian, X., Yan, R., Liu, Y., & Wang, S. (2023). A smart predict-then-optimize method for targeted and cost-effective maritime transportation. Transportation Research Part B-methodological, 172, 32-52. https://doi.org/10.1016/j.trb.2023.03.009.

6. Eruguz, A. S., Tan, T., & Van Houtum, G. (2017). A survey of maintenance and service logistics management: Classification and research agenda from a maritime sector perspective. Computers & Operations Research, 85, 184-205. https://doi.org/10.1016/j.cor.2017.03.003.

7. Mouschoutzi, M., & Ponis, S. T. (2022). A comprehensive literature review on spare parts logistics management in the maritime industry. The Asian Journal of Shipping and Logistics, 38(2), 71-83. https://doi.org/10.1016/j.ajsl.2021.12.003.

8. Kretschmann, L., Burmeister, H., & Jahn, C. (2017). Analyzing the economic benefit of unmanned autonomous ships: An exploratory cost-comparison between an autonomous and a conventional bulk carrier. Research in Transportation Business and Management, 25, 76-86. https://doi.org/10.1016/j.rtbm.2017.06.002.

9. Guo, S., Wang, Y., Dai, L., & Hu, H. (2023). All-electric ship operations and management: Overview and future research directions. eTransportation, 17, 100251. https://doi.org/10.1016/j.etran.2023.100251.

10. Veitch, E., & Alsos, O. A. (2022). A systematic review of human-AI interaction in autonomous ship systems. Safety Science, 152, 105778. https://doi.org/10.1016/j.ssci.2022.105778.

11. Heyer, A., D’Souza, F., Morales, C. L., Ferrari, G., Mol, J. M. C., & De Wit, J. (2013). Ship ballast tanks a review from microbial corrosion and electrochemical point of view. Ocean Engineering, 70, 188-200. https://doi.org/10.1016/j.oceaneng.2013.05.005.

12. Govindan, K., Kannan, D., Jørgensen, T. J. D., & Nielsen, T. (2022). Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence. Transportation Research Part E: Logistics and Transportation Review, 164, 102725. https://doi.org/10.1016/j.tre.2022.102725.

13. Chou, M., Hsu, Y., Hsu, C., & Ding, J. (2022). Reconstruction mechanism and strategy of global maritime green supply chain against the backdrop of nuclear pollution. Marine Pollution Bulletin, 185, 114235. https://doi.org/10.1016/j.marpolbul.2022.114235.

14. Esa, M. a. M., & Muhammad, M. (2023). Adoption of prescriptive analytics for naval vessels risk-based maintenance: A conceptual framework. Ocean Engineering, 278, 114409. https://doi.org/10.1016/j.oceaneng.2023.114409.

15. Golovan, A., Gritsuk, I., Popeliuk, V. P., Sherstyuk, O., Honcharuk, I., …, & Khudiakov, I. (2019). Features of Mathematical Modeling in the Problems of Determining the Power of a Turbocharged Engine According to the Characteristics of the Turbocharger. SAE International Journal of Engines. https://doi.org/10.4271/03-13-01-0001.

16. Daya, A., & Lazakis, I. (2023). Developing an advanced reliability analysis framework for marine systems operations and maintenance. Ocean Engineering, 272, 113766. https://doi.org/10.1016/j.oceaneng.2023.113766.

17. Karatuğ, C., Arslanoğlu, Y., & Soares, C. G. (2023). Design of a decision support system to achieve condition-based maintenance in ship machinery systems. Ocean Engineering, 281, 114611. https://doi.org/10.1016/j.oceaneng.2023.114611.

18. Mikhalevich, M., Yarita, A., Leontiev, D., Gritsuk, I., Bogomolov, V., Klimenko, V., & Saravas, V. (2019). Selection of rational parameters of automated system of robotic transmission clutch control on the basis of simulation modelling. In SAE technical paper series. https://doi.org/10.4271/2019-01-0029.

19. Volodarets, M., Gritsuk, I., Chygyryk, N., Belousov, E., Golovan, A., Volska, O., …, & Volodarets, O. (2019). Optimization of vehicle operating conditions by using simulation modeling software. In SAE technical paper series. https://doi.org/10.4271/2019-01-0099.

20. Gorobchenko, O., Fomin, O., Gritsuk, I., Saravas, V., Grytsuk, Y., Bulgakov, M., Volodarets, M., & Zinchenko, D. O. (2018). Intelligent Locomotive Decision Support System structure development and operation quality assessment. https://doi.org/10.1109/ieps.2018.8559487.

21. Golovan, A., Gritsuk, I., Rudenko, S., Saravas, V., Shakhov, A., & Shumylo, O. (2019). Aspects of Forming the Information V2I Model of the Transport Vessel. 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES), (pp. 390-393). Kremenchuk, Ukraine, 2019. https://doi.org/10.1109/MEES.2019.8896595.

22. Naumov, V., Zhamanbayev, B., Agabekova, D., Zhanbirov, Z., & Taran, I. (2021). Fuzzy-logic approach to estimate the passengers’ preference when choosing a bus line within the public transport system. Communications – Scientific Letters of the University of Žilina, 23(3), A150-A157. https://doi.org/10.26552/com.C.2021.3.A150-A157.

23. Taran, I., & Bondarenko, A. (2017). Conceptual approach to select parameters of hydrostatic and mechanical transmissions for wheel tractors designed for agricultural operations. Archives of transport, 41(1), 89-100. https://doi.org/10.5604/01.3001.0009.7389.

24. Taran, I. A., & Klimenko, I. Yu. (2014). Transfer ratio of double-split transmissions in case of planetary gear input. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (6), 60-66.

• < Prev
• Next >