Development of a hierarchical transportation planning model with local segmentation of orders and adaptive vehicle selection

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.351416

Keywords:

hierarchical transportation planning, order segmentation, vehicle selection

Abstract

The processes to plan transport deliveries in logistics systems involving hierarchical models have been investigated in this paper. The task to optimally plan freight delivery for transport and distribution logistics enterprises is associated with problems arising from fluctuations in demand, geographically distributed orders, as well as limited and heterogeneous resources.

The results of this study include the construction of a hierarchical model that allows for multi-level transportation planning, clustering of previously unclassified orders, and adjusting the choice of vehicles based on current conditions. Such adaptive choice of vehicles flexibly takes into account logistical constraints.

The findings indicate a reduction in transportation costs by 11.7% (p < 0.05). At the same time, it was found that under conditions of small samples, the stability of cluster solutions is limited and, therefore, additional verification and extended validation are required for their practical implementation.

The novelty of the proposed model is in the application of hierarchical decomposition of the multi-index transportation planning problem with the allocation of the global stage of cluster formation and the local stage of route planning. An approach to order clustering based on a limited sample of the closest applications in time and distance and an algorithm for adaptive selection of vehicles taking into account cost, carrying capacity and urgency of execution have been proposed.

The model built makes it possible to reduce the computational complexity of the problem compared to classical routing models while maintaining the interpretability of solutions at each stage due to transparent clustering rules. The scope of practical use of the results covers transport and logistics companies, delivery services, urban distribution systems, and retail logistics.

Author Biographies

Ilona Drach, Khmelnytskyi National University

Doctor of Technical Sciences

Department of Tribology, Automobiles and Materials Science

Oksana Kucheruk, Khmelnytskyi National University

PhD, Associate Professor

Department of Telecommunications, Media and Intellectual Technologies

Tetiana Kysil, Khmelnytskyi National University

PhD, Associate Professor

Department of Computer Engineering and Information Systems

Oleksandr Dykha, Khmelnytskyi National University

Doctor of Technical Sciences, Professor, Head of Department

Department of Tribology, Automobiles and Materials Science

Serhii Matiukh, Khmelnytskyi National University

PhD, Associate Professor

Rector

References

  1. Grazia Speranza, M. (2018). Trends in transportation and logistics. European Journal of Operational Research, 264 (3), 830–836. https://doi.org/10.1016/j.ejor.2016.08.032
  2. Voccia, S. A., Campbell, A. M., Thomas, B. W. (2019). The Same-Day Delivery Problem for Online Purchases. Transportation Science, 53 (1), 167–184. https://doi.org/10.1287/trsc.2016.0732
  3. Moslem, S., Saraji, M. K., Mardani, A., Alkharabsheh, A., Duleba, S., Esztergar-Kiss, D. (2023). A Systematic Review of Analytic Hierarchy Process Applications to Solve Transportation Problems: From 2003 to 2022. IEEE Access, 11, 11973–11990. https://doi.org/10.1109/access.2023.3234298
  4. Wang, J. (2024). Real-time Optimization Algorithm for Intelligent Logistics Transportation Routes Based on Big Data Analysis. 2024 International Conference on Machine Intelligence and Digital Applications, 193–199. https://doi.org/10.1145/3662739.3672306
  5. Kacher, Y., Singh, P. (2021). A Comprehensive Literature Review on Transportation Problems. International Journal of Applied and Computational Mathematics, 7 (5). https://doi.org/10.1007/s40819-021-01134-y
  6. Ekanayake, E. M. U. S. B., Daundasekara, W. B., Perera, S. P. C. (2022). An examination of different types of transportation problems and mathematical models. American Journal of Mathematical and Computer Modelling, 7 (3), 37–48. Available at: https://www.sciencepublishinggroup.com/article/10.11648/10071965
  7. Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., Akinyelu, A. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, 104743. https://doi.org/10.1016/j.engappai.2022.104743
  8. Khayya, E., Medarhri, I., Zine, R. (2024). A survey of the vehicle routing problem and its variants: formulations and solutions. Mathematical Modeling and Computing, 11 (1), 333–343. https://doi.org/10.23939/mmc2024.01.333
  9. Gutiérrez Rubiano, D. F., Hincapié Montes, J. A., León Villalba, A. F. (2019). Collaborative distribution: strategies to generate efficiencies in urban distribution - Results of two pilot tests in the city of Bogotá. DYNA, 86 (210), 42–51. https://doi.org/10.15446/dyna.v86n210.78931
  10. Rahman, M. M., Datta, P. (2025). Data-driven business strategies with the power of the K-means algorithm. International Journal of Higher Education Management, 11 (02). https://doi.org/10.24052/ijhem/v11n02/art-1
  11. Villalba, A. F. L., Rotta, E. C. G. L. (2022). Clustering and heuristics algorithm for the vehicle routing problem with time windows. International Journal of Industrial Engineering Computations, 13 (2), 165–184. https://doi.org/10.5267/j.ijiec.2021.12.002
  12. Chorna, O., Didyk, P., Titov, S., Titova, O. (2024). Usage of clustering algorithms for automating route planning in transportation routing tasks. Information Processing Systems, 1 (176), 115–123. https://doi.org/10.30748/soi.2024.176.14
  13. Wu, X., Cheng, C., Zurita-Milla, R., Song, C. (2020). An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering. International Journal of Geographical Information Science, 34 (9), 1822–1848. https://doi.org/10.1080/13658816.2020.1726922
  14. Pidchenko, S., Kucheruk, O., Drach, І., Pyvovar, O. (2024). Multi-criteria model for selection of optical linear terminals based on FUZZY TOPSIS method. Radioelectronic and Computer Systems, 2024 (1), 65–75. https://doi.org/10.32620/reks.2024.1.06
  15. Pidchenko, S., Kucheruk, O., Pyvovar, O., Stetsiuk, V., Mishan, V. (2023). A multi-criteria approach to decision-making in telecommunication network components selection. Radioelectronic and Computer Systems, 1, 155–165. https://doi.org/10.32620/reks.2023.1.13
  16. Drach, I., Dykha, O., Matiukh, S., Dykha, M. (2025). Determination of the range of angular velocities of the auto-balancing mode for a vertical rotor system with a Leblanc-type balancer. Eastern-European Journal of Enterprise Technologies, 2 (7 (134)), 66–75. https://doi.org/10.15587/1729-4061.2025.324793
  17. Holenko, K., Dykha, A., Voichyshyn, Y., Horbay, O., Dykha, M., Dytyniuk, V. (2024). Determining the characteristics of contact interaction between the two-row windshield wiper and a curvilinear glass surface. Eastern-European Journal of Enterprise Technologies, 1 (7 (127)), 48–59. https://doi.org/10.15587/1729-4061.2024.298204
  18. Ambrosio, L., Brué, E., Semola, D. (2024). Lectures on Optimal Transport. In UNITEXT. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-76834-7
  19. Mehlawat, M. K., Kannan, D., Gupta, P., Aggarwal, U. (2019). Sustainable transportation planning for a three-stage fixed charge multi-objective transportation problem. Annals of Operations Research, 349 (2), 649–685. https://doi.org/10.1007/s10479-019-03451-4
  20. Aardal, K. I., Iwata, S., Kaibel, V., Svensson, O. N. A. (2022). Combinatorial Optimization. Oberwolfach Reports, 18 (4), 2893–2954. https://doi.org/10.4171/owr/2021/53
  21. Abd Elazeem, A. E. M., Mousa, A. A. A., El-Shorbagy, M. A., Elagan, S. K., Abo-Elnaga, Y. (2021). Detecting All Non-Dominated Points for Multi-Objective Multi-Index Transportation Problems. Sustainability, 13 (3), 1372. https://doi.org/10.3390/su13031372
  22. Zitouni, R., Achache, M. (2017). A numerical comparison between two exact simplicial methods for solving a capacitated 4-index transportation problem. Journal of Numerical Analysis and Approximation Theory, 46 (2), 181–192. https://doi.org/10.33993/jnaat462-1116
  23. Dar, A., Selvakumar, K., Ramki, S., Karuppasamy, K. M., Ansari Rather, J. A., A. (2023). Optimizing Multi-Objective Multi-Index Transportation Problems: A Smart Algorithmic Solution with Lindo Software. RT&A, 18 (4 (76)), 154–167. https://doi.org/10.24412/1932-2321-2023-476-154-167
  24. Solving the Multi‐Objective Four‐Dimensional Transportation Problem using the Strength Pareto Evolutionary Algorithm (2024). Optimization in the Agri‐Food Supply Chain, 89–120. https://doi.org/10.1002/9781394316977.ch6
  25. Hakim, M., Zitouni, R. (2024). An approach to solve a fuzzy bi-objective multi-index fixed charge transportation problem. Kybernetika, 271–292. https://doi.org/10.14736/kyb-2024-3-0271
  26. Cao, J. (2022). Mathematical Model and Algorithm of Multi-Index Transportation Problem in the Background of Artificial Intelligence. Journal of Advanced Transportation, 2022, 1–11. https://doi.org/10.1155/2022/3664105
  27. Singh, B., Singh, A. (2023). Multi-Index Transportation Problem: an Overview of Its Variants, Solution Techniques and Applications. Journal Punjab Academy of Sciences, 23, 164–174. Available at: http://jpas.in/index.php/home/article/view/64
  28. Akimov, D. (2022). Application of the Decomposition Method for Solving the Five-Index Transportation Problem in Logistic Systems. Modern engineering and innovative technologies, 2 (37-02), 68-73. https://doi.org/10.30890/2567-5273.2025-37-02-059
  29. Ikotun, A. M., Habyarimana, F., Ezugwu, A. E. (2025). Cluster validity indices for automatic clustering: A comprehensive review. Heliyon, 11 (2), e41953. https://doi.org/10.1016/j.heliyon.2025.e41953
Development of a hierarchical transportation planning model with local segmentation of orders and adaptive vehicle selection

Downloads

Published

2026-02-27

How to Cite

Drach, I., Kucheruk, O., Kysil, T., Dykha, O., & Matiukh, S. (2026). Development of a hierarchical transportation planning model with local segmentation of orders and adaptive vehicle selection. Eastern-European Journal of Enterprise Technologies, 1(3 (139), 35–47. https://doi.org/10.15587/1729-4061.2026.351416

Issue

Section

Control processes