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Choosing the logistics chain structure for deliveries of bulk loads: case study of the Republic Kazakhstan

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Category: Content №3 2021

Last Updated on 23 June 2021

Published on 30 November -0001

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Authors:

B.Ramazan, orcid.org/0000-0001-8486-8736, Kazakh Academy of Transport and Communications, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

R.Mussaliyeva, orcid.org/0000-0001-8867-9932, Turan University, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Z.Bitileuova, orcid.org/0000-0001-9260-7034, Kazakh Academy of Transport and Communications, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.Naumov, orcid.org/0000-0001-9981-4108, Cracow University of Technology, Krakow, the Republic of Poland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it., 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 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. 2021, (3): 142 - 147

https://doi.org/10.33271/nvngu/2021-3/142

Abstract:

Purpose. The paper aims to develop the methodology for choosing the best available structure of a logistics chain for deliveries of bulk cargoes.

Methodology. The systematic approach is used to formalize the problem of choosing the optimal structure of a logistics chain: the total expenses are used to define the goal function, alternative logistics chain structures and numeric parameters of the request flow (the delivery distance and the consignment weight) are defined as control variables, and the random variables representing technological parameters of servicing processes are used to consider the influence of the environment on the logistics chain. The mathematical model defines the functional dependence of the total expenses on the entities within the delivery chain on the request flow parameters for two alternative structures: the delivery with transshipment in one freight terminal and the delivery through two terminals. By using functional analysis, we define the ranges of the request flow parameters where the use of a given logistics chain structure is characterized by minimal total expenses.

Findings. The experimental studies conducted considering two alternative logistics chain structures have shown that the better solution varies depending on the values of the request parameters. It allowed us to state that the ranges of the numeric parameters of the request flow may be defined to substantiate the optimal logistics chain structure for deliveries of bulk loads.

Originality. The dependencies of the total logistics expenses on the delivery distance and the consignment weight as the parameters of the flow of requests for bulk cargo transportation have been defined in the paper for the first time.

Practical value. The proposed methodological approach can be used by freight forwarders to substantiate the best option out of available alternative structures of a logistics chain for deliveries of loads with the given delivery distance and the consignment weight.

Keywords: logistics chain structure, freight forwarding, total logistics expenses, a flow of requests

References.

1. Agency for Strategic planning and reforms of the Republic of Kazakhstan Bureau of National statistics (n.d.). Retrieved from http://stat.gov.kz.

2. Ghorashi Khalilabadi, S.M., Zegordi, S.H., & Nikbakhsh, E. (2020). A multi-stage stochastic programming approach for supply chain risk mitigation via product substitution. Computers & Industrial Engineering, (149), 106786. https://doi.org/10.1016/j.cie.2020.106786.

3. Naumov, V. (2020). Substantiating the logistics chain structure while servicing the flow of requests for road transport deliveries. Sustainability, 12(4), 1635. https://doi.org/10.3390/su12041635.

4. Rajabion, L., Khorraminia, M., Andjomshoaa, A., Ghafouri-Azar,M., & Molavi, H. (2019). A new model for assessing the impact of the urban intelligent transportation system, farmers knowledge and business processes on the success of green supply chain management system for urban distribution of agricultural products. Journal of Retailing and Consumer Services, (50), 154-162. https://doi.org/10.1016/j.jretconser.2019.05.007.

5. Bakker, C., Zaitchik, B.F., Siddiqui, S.A., Hobbs, B.F., Broaddus,E.T., Neff, R.A., Haskett, J.D., & Parker, C.L. (2018). Shocks, seasonality, and disaggregation: Modelling food security through the integration of agricultural, transportation, and economic systems. Agricultural Systems, (164), 165-184. https://doi.org/10.1016/j.agsy.2018.04.005.

6. Lee, G., Lee, H.-W., & Lee, J. (2015). Greenhouse gas emission reduction effect in the transportation sector by urban agriculture in Seoul, Korea. Landscape and Urban Planning, (140), 1-7. https://doi.org/10.1016/j.landurbplan.2015.03.012.

7. Wu, J., & Haasis, H.D. (2018). The freight village as a pathway to sustainable agricultural products logistics in China. Journal of Cleaner Production, (196), 1227-1238. https://doi.org/10.1016/j.jclepro.2018.06.077.

8. Sun, J., Tang, J., Fu, W., Chen, Z., & Niu, Y. (2020). Construction of a multi-echelon supply chain complex network evolution model and robustness analysis of cascading failure. Computers & Industrial Engineering, (144), 106457. https://doi.org/10.1016/j.cie.2020.106457.

9. Dai, M., & Liu, L. (2020). Risk assessment of agricultural supermarket supply chain in big data environment. Sustainable Computing: Informatics and Systems, (28), 100420. https://doi.org/10.1016/j.suscom.2020.100420.

10. Huo, L., Guo, H., Cheng, Y., & Xie, X. (2020). A new model for supply chain risk propagation considering herd mentality and risk preference under warning information on multiplex networks. Physica A: Statistical Mechanics and its Applications, (545), 123506. https://doi.org/10.1016/j.physa.2019.123506.

11. Liu, H., Yang, R., Wu, J., & Chu, J. (2021). Total-factor energy efficiency change of the road transportation industry in China: A stochastic frontier approach. Energy, (219), 119612. https://doi.org/10.1016/j.energy.2020.119612.

12. Fierro, L.H., & Garca, J.I. (2020). Modelling of a multi-agent supply chain management system using Colored Petri Nets. Procedia Manufacturing, (42), 288-295. https://doi.org/10.1016/j.promfg.2020.02.095.

13. Yan, B., Chen, X., Cai, C., & Guan, S. (2020). Supply chain coordination of fresh agricultural products based on consumer behavior. Computers & Operations Research, (123), 105038. https://doi.org/10.1016/j.cor.2020.105038.

14. Mehmann, J., & Teuteberg, F. (2016). The fourth-party logistics service provider approach to support sustainable development goals in transportation a case study of the German agricultural bulk logistics sector. Journal of Cleaner Production, (126), 382-393. https://doi.org/10.1016/j.jclepro.2016.03.095.

15. Agrawal, A.K., & Yadav, S. (2020). Price and profit structuring for single manufacturer multi-buyer integrated inventory supply chain under price-sensitive demand condition. Computers & Industrial Engineering, (139), 106208. https://doi.org/10.1016/j.cie.2019.106208.

16. Magalhes, V., Ferreira, L.M., & Silva, C. (2021). Using a methodological approach to model causes of food loss and waste in fruit and vegetable supply chains. Journal of Cleaner Production, (283), 124574. https://doi.org/10.1016/j.jclepro.2020.124574.

17. Liu, L., Wang, H., & Xing, Sh. (2019). Optimization of distribution planning for agricultural products in logistics based on degree of maturity. Computers and Electronics in Agriculture, (160), 1-7. https://doi.org/10.1016/j.compag.2019.02.030.

18. Moubed, M., Boroumandzad, Y., & Nadizadeh, A. (2021). A Dynamic model for deteriorating products in a closed-loop supply chain. Simulation Modelling Practice and Theory, (108), 102269. https://doi.org/10.1016/j.simpat.2021.102269.

19. Darmawan, A., Wong, H.W., & Thorstenson, A. (2021). Supply chain network design with coordinated inventory control. Transportation Research Part E: Logistics and Transportation Review, (145), 102168. https://doi.org/10.1016/j.tre.2020.102168.

20. Sabraliev, N., Abzhapbarova, A., Nugymanova, G., Taran, I., & Zhanbirov, Zh. (2019). Modern aspects of modeling of transport routes in Kazakhstan. News of the National Academy of sciences of the Republic Kazakhstan, 2(434), 62-68. https://doi.org/10.32014/2019.2518-170X.39.

21. 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.

22. Naumov, V. (2018). Modeling demand for freight forwarding services on the grounds of logistics portals data. Transportation Research Procedia, 30, 324-331. https://doi.org/10.1016/j.trpro.2018.09.035.

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