Development of an approach to managing transport logistics at a construction site

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.361917

Keywords:

A*, construction logistics, hexagonal environment model, machine learning, multi-agent, STGNN

Abstract

This study investigates the process that forms a vehicle route in a complex logistics environment. The task addressed relates to the lack of a full-fledged logic in autonomous decision-making regarding operational routing in specialized logistics systems.

An analysis of modern transport logistics management systems revealed their limitations in terms of the efficiency and explainability of the logic of autonomous decision-making regarding route replanning. An approach to route formation has been devised that combines the modified A* algorithm, multi-agent reinforcement learning, and spatio-temporal graph convolutional networks (STGCNs).

Distinctive features of this approach are the combination of spatial-temporal forecasting, multi-agent decision-making, and modular system architecture, which provides adaptability, scalability, resistance to communication degradation, as well as explainability of transport routing decisions in closed reconfigurable networks.

Experimental comparison of the devised approach and analogs based on A* and STGCN, carried out on a simulation model of the logistics environment at a construction site in residential area, showed its statistical superiority in terms of the route travel time criterion. With a threshold of this criterion of 300 s, the proposed approach achieved this indicator in 90% of cases, and the approaches based on STGCN and A* – in 79% and 57% of cases. In this case, the value of the 95th percentile was 310 s, 360 s, and 410 s for the proposed approach, STGCN, and A*. These results are explained by the combination of network state prediction and adaptive route replanning.

The findings could be used in the design of intelligent transport logistics management systems at other facilities with dynamic transport infrastructure

Author Biographies

Pavlo Pasieka, Kyiv National University of Construction and Architecture

PhD Student

Department of Information Technologies of Design and Applied Mathematics

Oleksii Panko, Kyiv National University of Construction and Architecture

Candidate of Technical Sciences, Associate Professor

Department of Architectural Structures

Nataliia Poltorachenko, Kyiv National University of Construction and Architecture

Candidate of Technical Sciences, Associate Professor

Department of Information Technologies of Design and Applied Mathematics

Svitlana Terenchuk, Київський національний університет будівництва і архітектури

Кандидат фізико-математичних наук, професор

Кафедра інформаційних технологій проєктування та прикладної математики

Bohdan Yeremenko, Taras Shevchenko National University of Kyiv

Candidate of Technical Sciences, Associate Professor

Department of Management Technologies

References

  1. Nguyen, H. A. D., Ha, Q. P. (2022). Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey. Robotica, 41 (2), 486–510. https://doi.org/10.1017/s0263574722000339
  2. Mazurenko, R., Yeremenko, B. (2024). Intelligent traffic management system of a big city: ontology concept “decision models”. Management of Development of Complex Systems, 57, 174–180. https://doi.org/10.32347/2412-9933.2024.57.174-180
  3. Bortolini, R., Formoso, C. T., Viana, D. D. (2019). Site logistics planning and control for engineer-to-order prefabricated building systems using BIM 4D modeling. Automation in Construction, 98, 248–264. https://doi.org/10.1016/j.autcon.2018.11.031
  4. Whitlock, K., Abanda, F. H., Manjia, M. B., Pettang, C., Nkeng, G. E. (2021). 4D BIM for Construction Logistics Management. CivilEng, 2 (2), 325–348. https://doi.org/10.3390/civileng2020018
  5. Jiang, Y., Li, M., Li, M., Liu, X., Zhong, R. Y., Pan, W., Huang, G. Q. (2022). Digital twin-enabled real-time synchronization for planning, scheduling, and execution in precast on-site assembly. Automation in Construction, 141, 104397. https://doi.org/10.1016/j.autcon.2022.104397
  6. Choudhary, A. (2024). Internet of Things: a comprehensive overview, architectures, applications, simulation tools, challenges and future directions. Discover Internet of Things, 4 (1). https://doi.org/10.1007/s43926-024-00084-3
  7. Tuhaise, V. V., Tah, J. H. M., Abanda, F. H. (2023). Technologies for digital twin applications in construction. Automation in Construction, 152, 104931. https://doi.org/10.1016/j.autcon.2023.104931
  8. Le, T., Fan, R. (2023). Digital Twins for Logistics and Supply Chain Systems: Literature Review, Conceptual Framework, Research Potential, and Practical Challenges. https://doi.org/10.2139/ssrn.4516184
  9. de Carvalho, J. P., Dimitrakopoulos, R. (2021). Integrating Production Planning with Truck-Dispatching Decisions through Reinforcement Learning While Managing Uncertainty. Minerals, 11 (6), 587. https://doi.org/10.3390/min11060587
  10. Yeremenko, B., Mazurenko, R., Stetsyk, O., Buhrov, A. (2023). Intelligent Management of Traffic Flows in Large Cities. TRANSBALTICA XIII: Transportation Science and Technology, 33–42. https://doi.org/10.1007/978-3-031-25863-3_4
  11. Lin, K., Zhao, R., Xu, Z., Zhou, J. (2018). Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1774–1783. https://doi.org/10.1145/3219819.3219993
  12. Duszak, P., Siemiątkowska, B., Więckowski, R. (2021). Hexagonal Grid-Based Framework for Mobile Robot Navigation. Remote Sensing, 13 (21), 4216. https://doi.org/10.3390/rs13214216
  13. Zhang, Z., Zhang, J., Zhao, Y., Zhu, Z., Guo, J. (2024). A novel hexagonal grid map model and regenerated heuristic factor based strategy for intelligent manufacturing system’s AGV path planning problem solving. Computers & Industrial Engineering, 192, 110154. https://doi.org/10.1016/j.cie.2024.110154
  14. Schrader, M., Kumar, N., Collignon, N., Sørig, E., Yoon, S., Srivastava, A. et al. (2023). Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics. arXiv. https://doi.org/10.48550/arXiv.2301.12887
  15. Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1 (1), 269–271. https://doi.org/10.1007/bf01386390
  16. Hart, P., Nilsson, N., Raphael, B. (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4 (2), 100–107. https://doi.org/10.1109/tssc.1968.300136
  17. Stentz, A. (1995). The focussed D* algorithm for real-time replanning. In IJCAI'95: Proceedings of the 14th international joint conference on Artificial intelligence, 1652–1659. Available at: https://dl.acm.org/doi/10.5555/1643031.1643113
  18. Koenig, S., Likhachev, M., Furcy, D. (2004). Lifelong Planning A∗. Artificial Intelligence, 155 (1-2), 93–146. https://doi.org/10.1016/j.artint.2003.12.001
  19. Koenig, S., Likhachev, M. (2002). D*Lite. Eighteenth national conference on Artificial intelligence, 476–483. Available at: https://dl.acm.org/doi/10.5555/777092.777167
  20. Busoniu, L., Babuska, R., De Schutter, B. (2008). A Comprehensive Survey of Multiagent Reinforcement Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38 (2), 156–172. https://doi.org/10.1109/tsmcc.2007.913919
  21. Yu, C., Velu, A., Vinitsky, E., Gao, J., Wang, Y., Bayen, A., Wu, Y. (2022). The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games. Advances in Neural Information Processing Systems 35, 24611–24624. https://doi.org/10.52202/068431-1787
  22. Yu, J., LaValle, S. (2013). Structure and Intractability of Optimal Multi-Robot Path Planning on Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 27 (1), 1443–1449. https://doi.org/10.1609/aaai.v27i1.8541
  23. Rashid, T., Samvelyan, M., de Witt, C. S., Farquhar, G., Foerster, J., Whiteson, S. (2018). QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning. arXiv. https://doi.org/10.48550/arXiv.1803.11485
  24. Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., Mordatch, I. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. arXiv. https://doi.org/10.48550/arXiv.1706.02275
  25. Li, Y., Yu, R., Shahabi, C., Liu, Y. (2018). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. arXiv. https://doi.org/10.48550/arXiv.1707.01926
  26. Yu, B., Yin, H., Zhu, Z. (2018). Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 3634–3640. https://doi.org/10.24963/ijcai.2018/505
  27. Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C. (2019). Graph WaveNet for Deep Spatial-Temporal Graph Modeling. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 1907–1913. https://doi.org/10.24963/ijcai.2019/264
  28. Zhu, J., Wang, Q., Tao, C., Deng, H., Zhao, L., Li, H. (2021). AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting. IEEE Access, 9, 35973–35983. https://doi.org/10.1109/access.2021.3062114
  29. Likhachev, M., Gordon, G., Thrun, S. (2003). ARA*: anytime A* with provable bounds on sub-optimality. NIPS'03: Proceedings of the 17th International Conference on Neural Information Processing Systems. Available at: https://dl.acm.org/doi/10.5555/2981345.2981441
Development of an approach to managing transport logistics at a construction site

Downloads

Published

2026-06-30

How to Cite

Pasieka, P., Panko, O., Poltorachenko, N., Terenchuk, S., & Yeremenko, B. (2026). Development of an approach to managing transport logistics at a construction site. Eastern-European Journal of Enterprise Technologies, 3(3 (141), 41–53. https://doi.org/10.15587/1729-4061.2026.361917

Issue

Vol. 3 No. 3 (141) (2026): Control processes

Section

Control processes

License

Copyright (c) 2026 Pavlo Pasieka, Oleksii Panko, Nataliia Poltorachenko, Svitlana Terenchuk, Bohdan Yeremenko

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.