Shaping sustainable strategies of freight forwarding companies in the environment of the road transport market

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Authors:


V.Naumov1, orcid.org/0000-0001-9981-4108, Dnipro University of Technology, Dnipro, Ukraine

B.Umarova2, orcid.org/0009-0007-7609-3548, Toraighyrov University, Pavlodar, the Republic of Kazakhstan

I.Taran*1, orcid.org/0000-0002-3679-2519, Dnipro University of Technology, Dnipro, Ukraine, author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

R.Myrzageldiyev3,4, orcid.org/0009-0001-3345-1559, Kazakhstan University of Innovative and Telecommunication Systems, Almaty, the Republic of Kazakhstan; Kazakhstan Institute of Standardization and Metrology, Almaty, the Republic of Kazakhstan

Z.Tursymbekova5, orcid.org/0000-0001-6483-5451, International Transport and Humanitarian University, Almaty, the Republic of Kazakhstan

V.Lytvyn1, orcid.org/0000-0002-1572-9000, Dnipro University of Technology, Dnipro, Ukraine

* 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): 148 - 155

https://doi.org/10.33271/nvngu/2023-5/148



Abstract:



Purpose.
The paper aims to propose an approach to shape the sustainable strategies of freight forwarders under uncertainty of the stochastic environment of the transportation market.


Methodology.
The elements of the game theory were used to formalize the conflict situation, where the uncertainty of the transport market is considered as a game with nature. To design the model of the freight transportation market, the principles of the system’s theory were used in the representation of a forwarding company operating as an element within the macro-logistic system of the transport market. The methods of object-oriented programming were applied to develop the dedicated software for computer simulations of the forwarder’s operation within the market of transport services. The regression analysis was used as the main methodology to process the numeric results of experimental studies. The elements of functional analysis were applied to substantiate the sustainable strategy of a forwarding company in the considered example.


Findings.
The results of the conducted experiment allowed for determining the high-quality dependencies between the number of serviced requests and the number of dispatchers involved in the requests’ servicing for the case when the operators’ decisions are supported by the specialized software and for the case when decisions are made conventionally.


Originality.
The use of a game-theoretical approach in this study is based on the advanced simulations of a freight servicing process where the demand randomness and the servicing process stochasticity are integrally considered.


Practical value.
The proposed methodological approach is proposed to be used by forwarding companies to evaluate sustainable strategies when servicing clients within the given market. The use of the developed approach in practice allows forwarders to decrease the operational costs achieving the minimal negative impact on the environment.



Keywords:
sustainable development, game theory, freight forwarding, stochastic demand

References.


1. Ramazan, B., Mussaliyeva, R., Bitileuova, Z., Naumov, V., & Taran, I. (2021). Choosing the logistics chain structure for deliveries of bulk loads: Case study of the Republic Kazakhstan. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (3), 142-147. https://doi.org/10.33271/nvngu/2021-3/142.

2. 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 (Switzerland), 15(5), 4179. https://doi.org/10.3390/su15054179.

3. 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.

4. Nugymanova, G., Nurgaliyeva, M., Zhanbirov, Zh., Naumov, V., & Taran, I. (2021). Choosing a servicing company’s strategy while interacting with freight owners at the road transport market. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (1), 204-210. https://doi.org/10.33271/nvngu/2021-1/204.

5. Kadłubek, М. (2022). Relevance of modern technologies for sustainability-focused road freight transport service management in a competitive market. Procedia Computer Science, 207, 2013-2022. https://doi.org/10.1016/j.procs.2022.09.260.

6. Fulzele, V., & Shankar, R. (2022). Improving freight transportation performance through sustainability best practices. Transportation Research Part A: Policy and Practice, 165, 285-299. https://doi.org/10.1016/j.tra.2022.09.009.

7. Taherkhani, G., Bilegan, I. C., Crainic, T. G., Gendreau, V., & Rei, W. (2022). Tactical capacity planning in an integrated multi-stakeholder freight transportation system. Omega, 110, 102628. https://doi.org/10.1016/j.omega.2022.102628.

8. Ren, S., Choi, T-M., Lee, K-M., & Lin, L. (2020). Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach. Transportation Research Part E: Logistics and Transportation Review, 134, 101834. https://doi.org/10.1016/j.tre.2019.101834.

9. Mussin, A., Imashev, A., Matayev, A., Abeuov, Ye., Shaike, N., & Kuttybayev, A. (2023). Reduction of ore dilution when mining low-thickness ore bodies by means of artificial maintenance of the mined-out area. Mining of Mineral Deposits, 17(1), 35-42. https://doi.org/10.33271/mining17.01.035.

10. Wang, X., Kopfer, H., & Gendreau, M. (2014). Operational transportation planning of freight forwarding companies in horizontal coalitions. European Journal of Operational Research, 237(3), 1133-1141. https://doi.org/10.1016/j.ejor.2014.02.056.

11. Krajewska, M. A., & Kopfer, H. (2009). Transportation planning in freight forwarding companies: Tabu search algorithm for the integrated operational transportation planning problem. European Journal of Operational Research, 197(2), 741-751. https://doi.org/10.1016/j.ejor.2008.06.042.

12. Chow, K. H., Siu, W., Chan, C-H., & Chan, C. B. (2013). An argumentation-oriented multi-agent system for automating the freight planning process. Expert Systems with Applications, 40(10), 3858-3871. https://doi.org/10.1016/j.eswa.2012.12.042.

13. Reis, W. (2019). A new theoretical framework for integration in freight transport chains. Transport Reviews, 39(5), 589-610. https://doi.org/10.1080/01441647.2019.1573860.

14. Ren, S., Choi, T-M., Lee, K-M., & Lin, L. (2020). Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach. Transportation Research Part E: Logistics and Transportation Review, 134, 101834. https://doi.org/10.1016/j.tre.2019.101834.

15. Wang, S., & Yan, R. (2023). Fundamental challenge and solution methods in prescriptive analytics for freight transportation. Transportation Research Part E: Logistics and Transportation Review, 169, 102966. https://doi.org/10.1016/j.tre.2022.102966.

16. Tsolaki, K., Vafeiadis, K., Nizamis, A., Ioannidis, Dimosthe­nis K., & Tzovaras, D. (2023). Utilizing machine learning on freight transportation and logistics applications: A review. ICT Express, 9(3), 284-295. https://doi.org/10.1016/j.icte.2022.02.001.

17. Khakdaman, M., Rezaei, J., & Tavasszy, Lóránt A. (2020). Shippers’ willingness to delegate modal control in freight transportation. Transportation Research Part E: Logistics and Transportation Review, 141, 102027. https://doi.org/10.1016/j.tre.2020.102027.

18. Herold, David M., Fahimnia, B., & Breitbarth, T. (2023). The digital freight forwarder and the incumbent: A framework to examine disruptive potentials of digital platforms. Transportation Research Part E: Logistics and Transportation Review, 176, 103214. https://doi.org/10.1016/j.tre.2023.103214.

19. Günay, G. (2023). Shipment size and vehicle choice modeling for road freight transport: A geographical perspective. Transportation Research Part A: Policy and Practice, 173, 103732. https://doi.org/10.1016/j.tra.2023.103732.

20. Mohsen, Baha M. (2023). Multi-Criteria Decision System for the Selection of A Freight Forwarder Using AHP. Procedia Computer Science, 220, 135-144. https://doi.org/10.1016/j.procs.2023.03.020.

21. Kellner, F., Otto, A., & Brabänder, K. (2017). Bringing infrastructure into pricing in road freight transportation – A measuring concept based on navigation service data. Transportation Research Procedia, 25, 794-805. https://doi.org/10.1016/j.trpro.2017.05.458.

22. Matteis, T., Liedtke, G., & Wisetjindawat, W. (2016). A Framework for Incorporating Market Interactions in an Agent Based Model for Freight Transport. Transportation Research Procedia, 12, 925-937. https://doi.org/10.1016/j.trpro.2016.02.044.

23. Dewi, D. S., & Septiana, T. (2015). Workforce Scheduling Considering Physical and Mental Workload: A Case Study of Domestic Freight Forwarding. Procedia Manufacturing, 4, 445-453. https://doi.org/10.1016/j.promfg.2015.11.061.

24. Aguas, O., & Bachmann, C. (2022). Assessing the effects of input uncertainties on the outputs of a freight demand model. Research in Transportation Economics, 95, 101234. https://doi.org/10.1016/j.retrec.2022.101234.

25. Jóvér, V., Gáspár, L., & Fischer, S. (2022). Investigation of Tramway Line No. 1, in Budapest, Based on Dynamic Measurements. Acta Polytechnica Hungarica, 19(3), 65-76. https://doi.org/10.12700/APH.19.3.2022.3.6.

26. Szalai, S., Szürke, S. K., Harangozó, D., & Fischer, S. (2022). Investigation of deformations of a lithium polymer cell using the Digital Image Correlation Method (DICM). Reports in Mechanical Engineering, 3(1), 116-134. https://doi.org/10.31181/rme20008022022s.

27. Zuniga-Garcia, N., Ismael, A., & Stinson, M. (2023). A freight asset choice model for agent-based simulation models. Procedia Computer Science, 220, 704-709. https://doi.org/10.1016/j.procs.2023.03.092.

28. Zholmagambetov, N., Khalikova, E., Demin, V., Balabas, A., Abdrashev, R., & Suiintayeva, S. (2023). Ensuring a safe geomechanical state of the rock mass surrounding the mine workings in the Karaganda coal basin, Kazakhstan. Mining of Mineral Deposits, 17(1), 74-83. https://doi.org/10.33271/mining17.01.074.

29. Naumov, V., & Kholeva, O. (2017). Studying Demand for Freight Forwarding Services in Ukraine on the Base of Logistics Portals Data. Procedia Engineering, 187, 317-323. https://doi.org/10.1016/j.proeng.2017.04.381.

30. Naumov, V., & Kholeva, O. (2017). Forming the Strategies of Sustainable Development of Freight Forwarders at Transportation Market. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (3), 129-134.

31. Naumov, V. Java Code for Simulations of the Freight Forwarding Processes. Retrieved from https://www.academia.edu/31832379.

 

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