Management system for neutralizing the impact of risks on logistics processes during their dynamic changes
/ 0
- Details
- Category: Content №6 2022
- Last Updated on 26 December 2022
- Published on 26 December 2022
- Hits: 2593
Authors:
Y.Mazur*, orcid.org/0000-0002-4728-4640, Interregional Academy of Personnel Management, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
M.Chaikovska, orcid.org/0000-0002-9490-5112, Odesa I.I.Mechnikov National University, Odesa, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
A.Zaderei, orcid.org/0000-0002-9660-986X, National University Odesa Maritime Academy, Odesa, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
V.Khrustalova, orcid.org/0000-0001-8522-8810, State University of Trade and Economics, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
I.Shtunder, orcid.org/0000-0001-7778-3072, State University of Trade and Economics, Kyiv, 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.
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (6): 170 - 175
https://doi.org/10.33271/nvngu/2022-6/170
Abstract:
Purpose. To develop an algorithm for choosing alternative routes with dynamic changes in the risks of cargo transportation. To propose a structure of the management system of logistics departments and an enterprise in general to neutralize risks.
Methodology. The method of mathematical formalization was used to form a mathematical model of management of transportation routes with changes in threats in real time; the method of a system approach to the enterprises business processes was used to take into account their impact on logistics processes; risk stratification method for evaluating the efficiency of logistics operations; the method of using the matrix of vectors made it possible to form a mathematical approach to solving a dynamic logistic problem and to considering it as a time-dispersed system; the method for dividing parameters by measurement scales allowed using a unified mathematical approach to the formalization of the problem; the method of sequential approximation made it possible to choose the most acceptable options for making management decisions.
Findings. It is established that the level of risks can change from minimal to unacceptable in real time, which proves the importance of assessing both the degree of risk and the rate of its change. An approach is proposed to coordinate the proposed alternative routes, taking into account the requirements of various structural departments, achieving both risk reduction, and ranking of goals, ensuring less time for transportation. The formation of a decision tree on logistics chains and continuous monitoring of risks is substantiated.
Originality. An algorithm for selecting alternative routes with dynamic changes in cargo transportation risks is developed. Astructure of the management system for logistics departments and enterprises to neutralize risks is proposed.
Practical value. The proposed approach makes it possible to predict risks in the face of their dynamic changes and to ensure their effective management.
Keywords: cargo transportation, management system, logistics processes, neutralizing risks, mathematical model
References:
1. Ardakani, A.A., & Fei, J. (2020). A systematic literature review on uncertainties in cross-docking operations. Modern Supply Chain Research and Applications, 2(1), 2-22. https://doi.org/10.1108/MSCRA-04-2019-0011.
2. Gardas, B.B., Raut, R.D., & Narkhede, B. (2018). Reducing the exploration and production of oil: Reverse logistics in the automobile service sector. Sustainable Production and Consumption, 16, 141-153. https://doi.org/10.1016/j.spc.2018.07.005.
3. Perevozova, I., Minakova, S., Obelnytska, K., Mykhailyshyn, L., & Morozova, O. (2021). Mathematical modeling of multimodal transportation in Ukraine using methods of the graph theory. E3S Web of Conferences, 255, 01030. https://doi.org/10.1051/e3sconf/202125501030.
4. Prokopenko, O., Shmorgun, L., Kushniruk, V., Prokopenko, M., Slatvinska, M., & Huliaieva, L. (2020). Business Process Efficiency in a Digital Economy. International Journal of Management, 11(3), 122-132. https://doi.org/10.34218/IJM.11.3.2020.014.
5. Abdi, A., Abdi, A., Akbarpour, N., Amiri, A. S., & Hajiaghaei-Keshteli, M. (2020). Innovative approaches to design and address green supply chain network with simultaneous pick-up and split delivery. Journal of Cleaner Production, 250(3), 119437. https://doi.org/10.1016/j.jclepro.2019.119437.
6. Gelareh, S., Glover, F., Guemri, O., Hanafi, S., Nduwayo, P., & Todosijevic, R. (2020). A comparative study of formulations for a cross-dock door assignment problem. Omega. The International Journal of Management Science, 919, 1-17. https://doi.org/10.1016/j.omega.2018.12.004.
7. Tavallali, P.A., Feylizadeh, M.R., & Amindoust, A. (2020). Presenting a mathematical programming model for routing and scheduling of cross-dock and transportation. Polish journal of management studies, 22(1), 545-564. https://doi.org/10.17512/pjms.2020.22.1.35.
8. Tabatabaeia, S.M., Safib, M., & Nikabadia, M.S. (2021). A mathematical model for scheduling oftransportation, routing, and cross-docking in the reverse logistics network of the green supply chain. International Journal of Analysis and Applications, 12(2), 1909-1927. https://doi.org/10.22075/ijnaa.2021.5325.
9. Kkolu, I., & ztrk, N. (2019). A hybrid meta-heuristic algorithm for vehicle routing and packing problem with cross-docking. Journal of Intelligent Manufacturing, 30(8), 2927-2943. https://doi.org/10.1007/s10845-015-1156-z.
10. Liao, T.Y. (2018). Reverse logistics network design for product recovery and remanufacturing. Applied Mathematical Modelling, 60, 145-163. https://doi.org/10.1016/j.apm.2018.03.003.
11. Wang, Y., Sun, Y., Guan, X., & Guo, Y. (2021). Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing. Journal of Advanced Transportation, 2021(7), 1-20. https://doi.org/10.1155/2021/8280686.
12. Euchi, J. (2020). Hybrid adaptive memory programming to optimise the multi-commodity many to many vehicle routing problem. International Journal of Mathematics in Operational Research, 17(4), 492. https://doi.org/10.1504/IJMOR.2020.110840.
13. Nozari, H., Tavakkoli-Moghaddam, R., & Gharemani-Nahr, J. (2022). A Neutrosophic Fuzzy Programming Method to Solve a Multi-Depot Vehicle Routing Model under Uncertainty during the COVID-19 Pandemic. International Journal of Engineering, Transactions, 35, 02. https://doi.org/10.5829/IJE.2022.35.02B.12.
14. Yu, H., & Solvang, W. (2018). Incorporating flexible capacity in the planning of a multi-product multi-echelon sustainable reverse logistics network under uncertainty. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2018.07.019.
15. Rattanamanee, T., Nanthavanij, S., & Ammarapala, V. (2018). Vehicle Dispatching for Minimizing Arrival Conflicts in Multi-supplier Logistics Network. Arabian Journal for Science and Engineering, 43, 3187-198. https://doi.org/10.1007/s13369-017-2957-5.
16. Rahimi, M., & Ghezavati, V. (2018). Sustainable multi-period reverse logistics network design and planning under uncertainty utilizing conditional value at risk (CVaR) for recycling construction and demolition waste. Journal of cleaner production, 172, 1567-1581.
17. Kozachenko, D., Malashkin, V., Berezovyi, M., & Borycheva, S. (2021). Analysis of logistic risks of railway transport of the mining and concentrating plant under conditions of increasing production volumes of finished products. Collection of scientific works of DNUZT named after Acad. V. Lazaryan, 21, 41-48. https://doi.org/10.15802/tstt2021/237650.
18. Nitsenko, V., Kotenko, S., Hanzhurenko, I., Mardani, A., Stashkevych, I., & Karakai, M. (2020). Mathematical Modeling of Multimodal Transportation Risks I.n Ghazali R., Nawi N., Deris M., Abawajy J. (Eds.) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, (pp. 439-447). Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_41.
19. Kotenko, S., Nitsenko, V., Hanzhurenko, I., & Havrysh, V. (2020). The Mathematical Modeling Stages of Combining the Carriage of Goods for Indefinite, Fuzzy and Stochastic Parameters. International Journal of Integrated Engineering, 12(7), 173-180. https://doi.org/10.30880/ijie.2020.12.07.019.
20. Nitsenko, V., Kotenko, S., Hanzhurenko, I., & Ingram, K.L. (2020). Determination of Weight Coefficients for Stochastic and Fuzzy Risks for Multimodal Transportation. Journal of Physics: Conference Series, 1529, 032007. https://doi.org/10.1088/1742-6596/1529/3/032007.
Newer news items:
Older news items:
- The impact of internationalization to improve and ensure quality education: a case study of Daffodil International University (Bangladesh) - 25/12/2022 02:37
- The impact of professional accountancy organizations on the quality of accounting education - 25/12/2022 02:37
- Legal management and regulation of the activities of professional participants in the stock market of Ukraine - 25/12/2022 02:37
- The impact of the economic and COVID-19 crises on the Visegrad Group countries - 25/12/2022 02:37
- Digital technologies and their impact on economic and social spheres in Ukraine - 25/12/2022 02:37
- Improving transport logistics of extractive industry products in the context of capacity constraints on the railways - 25/12/2022 02:37
- Computer modeling of territory flooding in the event of an emergency at Seredniodniprovska Hydroelectric Power Plant - 25/12/2022 02:37
- Study on accumulation of heavy metals by green plantations in the conditions of industrial cities - 25/12/2022 02:37
- Heavy metals removal using natural zeolite adsorption from Tigris river water at Samarra city (Iraq) - 25/12/2022 02:37
- Improvement of the safe work system - 25/12/2022 02:37
Archive