This article proposes an intelligent mivar decision-making system (MDMS) designed for the optimized distribution and transportation of cargo by groups of warehouse robots. This mivar decision-making system integrates three groups of different warehouse robots: the loader robot (RP), the transporter robot (RT), and the unloader robot (RR). The selection and determination of the state of each robot (loader robot, transporter robot, and unloader robot) are based on corresponding calculations performed using specially developed algorithms. These algorithms are based on a series of key equation systems, such as the transporter robot equation system, the loader robot equation system, the unloader robot equation system, and the command variable system. The equation systems take into account the robot's state, operational capability, ability to complete cargo transportation, compatibility for cargo transportation, etc. Additionally, the Manhattan distance is considered, which helps determine the robot's ability to complete its task. The article provides a detailed description of the equation systems and calculation algorithms, as well as a formalized description of the domain in which the mivar logical artificial intelligence system operates. The logical schematic of the MDMS system and decision-making rules are also outlined, which aid in robot selection, making the system more efficient. Experimental results show that this system can function normally according to pre-established logic and objectives. It accurately completed all distribution tasks, demonstrating good stability and reliability.
1. Binos T., Adamopoulos A., Bruno V. Decision Support Research in Warehousing and Distribution: A Systematic Literature Review. International Journal of Information Technology and Decision Making. 2020;19(3):653–693. https://doi.org/10.1142/S0219622020300013
2. Li Zh., Barenji A.V., Jiang J., Zhong R.Y., Xu G. A Mechanism for Scheduling Multi Robot Intelligent Warehouse System Face with Dynamic Demand. Journal of Intelligent Manufacturing. 2020;31(2):469–480. https://doi.org/10.1007/s10845-018-1459-y
3. Tubis A.A., Rohman Ju. Intelligent Warehouse in Industry 4.0–Systematic Literature Review. Sensors. 2023;23(8). https://doi.org/10.3390/s23084105
4. Gong S. Mivar Decision-Making System for Distribution and Transportation of Cargo by a Team of Warehouse Robots. Sistemy upravleniya i informatsionnye tekhnologii. 2025;(2):23–29. (In Russ.).
5. Varlamov O.O. Evolyutsionnye bazy dannykh i znanii dlya adaptivnogo sinteza intellektual'nykh sistem. Mivarnoe informatsionnoe prostranstvo. Moscow: Radio i svyaz'; 2002. 286 p. (In Russ.).
6. Antonova A.A., Varlamov O.O. Mivar Expert System for Supporting Personnel Decision-Making in the Production of Planetary Gearboxes. Modeling, Optimization and Information Technology. 2025;13(1). (In Russ.). https://doi.org/10.26102/2310-6018/2025.48.1.042
7. Podoprigorova N.S., Kozyrev S.A., Podoprigorova S.S., et al. Development of a Mivar Expert System for Selecting a Consensus Algorithm for Distributed Lists. Problems of Artificial Intelligence. 2024;(4):126–138. (In Russ.). https://doi.org/10.24412/2413-7383-2024-4-126-138
8. Kotsenko A.A. Development of a Method for Creating a Mivar Decision-Making System for Planning Robot Routes in a Three-Dimensional Logical Space. Informatsiya i obrazovanie: granitsy kommunikatsii. 2023;(15):301–304. (In Russ.).
9. Shen Q., Gong Sh., Varlamov O.O., Adamova L.E., Balenko E.G. Dynamic Robot Path Planning Based on Semantic Object Detection Using Mivar Expert System. Problems of Artificial Intelligence. 2024;(4):164–176. (In Russ.). https://doi.org/10.24412/2413-7383-2024-4-164-176
10. Kotsenko A., Andreev A., Kim R., et al. Route Planning of Autonomous Robots in Three-Dimensional Logic Space Using Mivar Technologies. In: E3S Web of Conferences: Volume 515 (2024): International Scientific Conference Transport Technologies in the 21st Century (TT21C-2024) "Actual Problems of Decarbonization of Transport and Power Engineering: Ways of Their Innovative Solution", 08–10 April 2024, Rostov-on-Don, Russia. EDP Sciences; 2024. https://doi.org/10.1051/e3sconf/202451504018
11. Aladin D.V., Aladina E.V., Chuvikov D.A., Varlamov O.O., Adamova L.E. Creating a "Logical Intelligent Plant Care System" in Digital Agriculture Based on Mivar Approach. In: IOP Conference Series: Earth and Environmental Science: 2021 International Conference on World Technological Trends in Agribusiness (WTTA 2021), 29–30 March 2021, Omsk, Russia. IOP Publishing; 2022. https://doi.org/10.1088/1755-1315/954/1/012004
12. Ann Mathews R., Aleena Kv A.Kv., Abhilash A., Dev Nand D D.N.D., Devarajan K. Survey on Warehouse Monitoring and Management Using AI. International Journal of Advances in Engineering and Management. 2024;6(11):391–397.
13. Boluchevskaya O.A., Miloshenko O.V. Contemporary Issues of Robots. Modeling, Optimization and Information Technology. 2013;(3). (In Russ.). URL: https://moit.vivt.ru/wp-content/uploads/2014/01/Boluchevskaya_Miloshenko_3_13_1.pdf
14. Fang Yu., De Koster R., Roy D., Yu Yu. Dynamic Robot Routing and Destination Assignment Policies for Robotic Sorting Systems. Transportation Science. 2025;59(3):451–687. https://doi.org/10.1287/trsc.2023.0458
15. Han M.H., Yakunin A.N. Object Detection and Tracking When Constructing Mobile Robot Motion Trajectory Using Image Processing. Modeling, Optimization and Information Technology. 2023;11(2). (In Russ.). https://doi.org/10.26102/2310-6018/2023.41.2.027
16. Lukin D.S., Kosenko E.Yu. A Neural Network Method for Path Planning in a Two-Dimensional Space. Analysis and Data Processing Systems. 2023;(4):55–68. (In Russ.). https://doi.org/10.17212/2782-2001-2023-4-55-68
17. Lavlinskaya O.Y., Bernikov V.V., Grigorova O.N. OpenMP Parallel Calculations in Algorithms for Solving Shortest Paths Problem. Modeling, Optimization and Information Technology. 2019;7(2). (In Russ.). https://doi.org/10.26102/2310-6018/2019.25.2.004
18. Isankin M.A., Malikov A.I. The Control and State Observer Design for Two Links Robot-Manipulator With Non Rigid Connection. Vestnik Kazanskogo gosudarstvennogo tekhnicheskogo universiteta im. A.N. Tupoleva. 2016;72(3):112–121. (In Russ.).
19. Preobrazhenskiy A.P. The Characteristics of the Information System of Warehouse Facilities. Modeling, Optimization and Information Technology. 2016;(3). https://moit.vivt.ru/wp-content/uploads/2016/10/Preobrazhensky_3_16_1.pdf
