DECISION-MAKING MODEL FOR MULTITASKING WAREHOUSE ROBOTS BASED ON A FOUR-LAYER COLLABORATIVE OPTIMIZATION FRAMEWORK

S. Gong 1,2

About the authors

S. Gong 1,2

1Bauman Moscow State Technical University 105005, 2-ya Baumanskaya st. bld. 5, structure 1, Moscow, Russia 2JSC M. A. Kartsev Research Institute of Computing Systems 117437, Profsoyuznaya st. bld. 108, Moscow, Russia

Download PDF

UDC: 004.89 + 007.52 + 681.518
DOI: 10.24412/2413-7383-2025-3-38-100-112
Language: Russian

Abstract: This paper proposes an original model based on a four -layer collaborative optimization framework, which integrates the mivar decision -making system and multi -level optimization mechanisms, overcoming the trade -off between dynamic response and computational complexity in traditional methods. A dynamic priority mechanism is employed to differentiate between urgent and large-scale cargo; a dual-mode optimization engine is considered, which addresses the issue of combinatorial explosion in limited -scale tasks ( n≤6) through global enumeration and a hybrid strategy combining nearest neighbor and 2-opt approaches. Experiments conducted in a simulated warehouse environment with 100 heterogeneous orders demonstrated that the model effectively generates 42 task group s (single/two/three/four-task groups accounting for 16.7%/40.5%/26.2%/16.6% respectively), with a total transport distance of 5436 meters (averaging 129.4 meters per group). This structure provides an optimization paradigm for intelligent warehousing systems, combining rules and models, and future research will be exten ded to scenarios involving heterogeneous robot planning and multi -robot transport of oversized cargo.

Keywords: mivar, mivar technology, dynamic task allocation, mivar decision-making system, logical AI, intelligent warehousing, multi-task robots, four-layer optimization

References

1. Popova, T. A. Digital technologies: a modern stage in the development of warehouse logistics / T. A. Popova // New in economic cybernetics. – 2024. – No. 4. – pp. 82-100. – DOI 10.5281/zenodo.15101877. – EDN LOFHTJ.
2. Mishchenko, E. V. Automated warehouse systems: optimization of logistics processes using modern technologies / E. V. Mishchenko, I. M. Tolmachev // Problems of energy supply, automation, informatization, and environmental management in agriculture: Proceedings of the international scientific and technical conference, Bryansk, May 16–17, 2024. – Bryansk: Bryansk State Agrarian University, 2024. – P. 283-288. – EDN UBXZIA.
3. Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges / X. An, C. Wu, Ya. Lin [et al.] // IEEE Open Journal of the Computer Society. – 2023. – Vol. 4. – P. 23-
4. – DOI 10.1109/ojcs.2023.3238324. – EDN GIZDYU.
5. Reshetnikova, M. S. The global industrial robotics market: development trends and volume forecast / M. S. Reshetnikova, I. A. Pugacheva // Research in Economic Anthropology. – 2022. – Vol. 42. – P. 187-
6. – DOI 10.1108/S0190-128120220000042018. – EDN IUEUGZ. 3Г Ш. Гун
7. Voevodskaya, A. S. System analysis as a decision-making method in the logistics design of warehouse systems / A. S. Voevodskaya // Economics and Business: Theory and Practice. – 2020. – No. 4-1(62). – P. 68-72. – DOI 10.24411/2411-0450-2020-10258. – EDN AJYKKJ.
8. A Multi-Robot Task Allocation Method Based on the Synergy of the K-Means++ Algorithm and the Particle Swarm Algorithm / Y. Yuan, P. Yang, H. Jiang, T. Shi // Biomimetics. – 2024. – Vol. 9, No. 11. – P. 694. – DOI 10.3390/biomimetics9110694. – EDN QMGXPD.
9. Orr, Ja. Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey / Ja. Orr, A. Dutta // Sensors. – 2023. – Vol. 23, No. 7. – P. 3625. – DOI 10.3390/s23073625. – EDN AHGHYH.
10. Galati, G. Auction-Based Task Allocation and Motion Planning for Multi-Robot Systems with Human Supervision / G. Galati, S. Primatesta, A. Rizzo // Journal of Intelligent and Robotic Systems. – 2023. – Vol. 109, No. 2. – P. 24. – DOI 10.1007/s10846-023-01935-x. – EDN VTCNNS.
11. Pokintelitsa, A. E. Problems and specifics of data reduction in autonomous robotic systems / A. E. Pokintelitsa // Problems of Artificial Intelligence. – 2023. – No. 1(28). – P. 31-41. – EDN XLGXQX.
12. Lewczuk, K. Energy Consumption in a Distributional Warehouse: A Practical Case Study for Different Warehouse Technologies / K. Lewczuk, M. Kłodawski, P. Gepner // Energies. – 2021. – Vol. 14, No. 9. – P. 2709. – DOI 10.3390/en14092709. – EDN NTIXOI.
13. Nikitina, A. A. Object detection by intelligent robots in rapidly changing environments / A. A. Nikitina, S. I. Ulanov // Problems of Artificial Intelligence. – 2023. – No. 3(30). – P. 36-43. – DOI 10.34757/2413- 7383.2023.30.3.003. – EDN ALLVUM.
14. A Novel Integrated Path Planning Algorithm for Warehouse AGVs / W. Tao, Ya. Daichuan, L. Weifeng [et al.] // Chinese Journal of Electronics. – 2021. – Vol. 30, No. 2. – P. 331-338. – DOI 10.1049/cje.2021.02.002. – EDN ROUXYH.
15. Sineglazov, V. M. Hybrid neural network optimization system based on ant algorithms / V. M. Sineglazov, O. I. Chumachenko, D. M. Omelchenko // Electronics and Control Systems. – 2020. – Vol. 2, No. 64. – P. 61-67. – DOI 10.18372/1990-5548.64.14857. – EDN GPCSKM.
16. Hybrid variable neighborhood search for automated warehouse scheduling / I. Davydov, Yu. Kochetov, D. Tolstykh [et al.] // Optimization Letters. – 2023. – Vol. 17, No. 9. – P. 2185-2199. – DOI 10.1007/s11590-022-01921-6. – EDN STIGTP.
17. Performance analysis of clustering methods for balanced multi-robot task allocations / E. Murugappan, N. Subramanian, S. Rahman [et al.] // International Journal of Production Research. – 2021. – DOI 10.1080/00207543.2021.1955994. – EDN GTNKUP.
18. A survey on multi-robot coverage path planning for model reconstruction and mapping / R. Almadhoun, L. Seneviratne, Y. Zweiri, T. Taha // SN Applied Sciences. – 2019. – Vol. 1, No. 8. – DOI 10.1007/s42452-019-0872-y. – EDN EZAWPF.
19. Varlamov, O. O. Evolutionary databases and knowledge bases for the adaptive synthesis of intelligent systems. Mivar information space / O. O. Varlamov. – Moscow: Scientific and Technical Publishing House “Radio i Svyaz”, 2002. – 286 p. – ISBN 5-256-01650-4. – EDN RWTCOP.
20. Kolpakidi, N. A. Development of a decision support system for biochemical blood analysis / N. A. Kolpakidi, A. A. Kotsenko // International Journal of Information Technologies and Energy Efficiency. – 2025. – Vol. 10, No. 4(54). – P. 117-128. – EDN RQBJUS.
21. Varlamov, O. “Brains” for Robots: Application of the Mivar Expert Systems for Implementation of Autonomous Intelligent Robots / O. Varlamov // Big Data Research. – 2021. – Vol. 25. – P. 100241. – DOI 10.1016/j.bdr.2021.100241. – EDN HRYLWL.
22. Gorodetsky, A. E. Optimization of robot-router movement considering driver temperament / A. E. Gorodetsky, I. L. Tarasova // Problems of Artificial Intelligence. – 2023. – No. 1(28). – P. 18-30. – EDN BTDZSK.
23. Route planning of autonomous robots in three-dimensional logic space using mivar technologies / A. Kotsenko, A. Andreev, R. Kim [et al.] // E3S Web of Conferences. – 2024. – Vol. 515. – P. 04018. – DOI 10.1051/e3sconf/202451504018. – EDN GMEYDO.
24. Dynamic robot trajectory planning based on semantic object detection using the mivar expert system / C. Shen, S. Gong, O. O. Varlamov [et al.] // Problems of Artificial Intelligence. – 2024. – No. 4(35). – P. 164-176. – DOI 10.24412/2413-7383-2024-4-164-176. – EDN DHVOFC.
25. Automated formation of discipline sequences for higher ecological and agricultural education using mivar expert systems / M. Peresypkina, O. Varlamov, A. Kotsenko [et al.] // Bio Web of Conferences: International Scientific Conference on Biotechnology and Food Technology (BFT-2024), Saint Petersburg, September 3–6, 2024. Vol. 130. – Les Ulis: EDP Sciences, 2024. – P. 08032. – DOI 10.1051/bioconf/202413008032. – EDN ERQVZN.
26. Development of a mivar expert system for selecting consensus algorithms for distributed ledgers / N. S. Podoprigorova, S. A. Kozyrev, S. S. Podoprigorova [et al.] // Problems of Artificial Intelligence. – 2024. – No. 4(35). – P. 126-138. – DOI 10.24412/2413-7383-2024-4-126-138. – EDN AVXOTO. Модель принятия решений для многозадачных складских роботов… 3Г
27. Development of a mivar expert system for implementing ethical aspects of artificial intelligence in credit scoring / M. S. Torzhkov, Yu. P. Koroleva, A. V. Baldin [et al.] // Problems of Artificial Intelligence. – 2024. – No. 4(35). – P. 139-150. – DOI 10.24412/2413-7383-2024-4-139-150. – EDN BHOQXX.
28. Integrated AI system based on mivar and neural networks for defect detection in bakery production / A. M. Golobokov, A. A. Kokhanov, I. D. Kats [et al.] // Problems of Artificial Intelligence. – 2025. – No. 1(36). – P. 27-42. – DOI 10.24412/2413-7383-27-42. – EDN QAZTYW.
29. Mivar expert system for intellectualization of automotive wheel rim production / A. N. Demidov, V. D. Chernenko, M. K. Votintsev [et al.] // Problems of Artificial Intelligence. – 2025. – No. 1(36). – P. 43-
30. – DOI 10.24412/2413-7383-43-57. – EDN HCKKLJ.
31. Chuvikov, D. A. Development of a platform for reconstruction and examination of accident events in road traffic accidents based on logical artificial intelligence / D. A. Chuvikov // Problems of Artificial Intelligence. – 2024. – No. 2(33). – P. 59-77. – DOI 10.24412/2413-7383-2024-2-59-77. – EDN EUPPZY.
32. Mikhaleva, U. A. Application of the mivar approach in systems for countering pretexting / U. A. Mikhaleva // Telecommunications. – 2024. – No. 3. – P. 38-40. – DOI 10.31044/1684-2588-2024-0-3- 38-40. – EDN VDTWAZ.
33. Gong, S. Mivar decision-making system for distribution and transportation of goods by a team of

Issue: 3(38)'2025
Section: SYSTEM ANALYSIS, CONTROL AND INFORMATION PROCESSING, STATISTICS
How to cite: S. Gong 1,2. DECISION-MAKING MODEL FOR MULTITASKING WAREHOUSE ROBOTS BASED ON A FOUR-LAYER COLLABORATIVE OPTIMIZATION FRAMEWORK // Problems of Artificial Intelligence. - 2025. - № 3 (38). - P. 100-112. - https://paijournal.guiaidn.ru/en/2025/3(38)-9.html

Back to issue 3(38)'2025