Development of a mivar expert system for planning shop resources and analysis of deviations

To create mechanical engineering artificial intelligence, mivar technologies of logical artificial intelligence are used. The production process is often accompanied by a large number of events, and various types of deviations and interference directly or indirectly affect the stable and efficient operation of production, and also lead to a decrease in product quality. Predicting variances and disturbances in production planning is a research problem that is the basis of resource planning for production systems. There is a known approach to solving optimization problems of resource allocation of production systems based on the construction of logical inference in a mivar knowledge base, which represents a resource allocation plan. This paper analyzes the deviations and/or disturbances caused by production interference on the shop floor, namely materials, personnel, equipment, processes, and so on, and proposes a definition of production interference in the shop floor production environment. A significant degree of interference results in delays in product deliveries, reductions in quality levels and other deviations from the planned production plan. A mivar expert system has been developed to predict deviations in production processes after planning workshop resources. The expert system was developed using the software package KESMI Wi!Mi "Razumator". Deviations in the production environment were analyzed, a system of factors influencing deviations was established, and a corresponding mivar model for predicting production deviations in the workshop was built. The use of a mivar expert system effectively and quickly solves the problem of decision support based on flexible complex calculations when calculating weights. Therefore, the mivar expert system plays a critical role in predicting interference in the planning of workshop operations, significantly increasing the efficiency of the entire enterprise management system.

1. Varlamov O.O., Krivosheev O.V., Trischenkov A.V. et al. Mechanical engineering artificial intelligence as a new direction for full product life cycle systems. In: MIVAR'22: Sbornik nauchnykh statei. Moscow: INFRA-M; 2022. pp. 363–369. (In Russ.).

2. Varlamov O.O., Krivosheev O.V., Trischenkov A.V. et al. Digitalization of the agro-industrial complex and mechanical engineering AI. In: MIVAR'22: Sbornik nauchnykh statei. Moscow: INFRA-M; 2022. pp. 390–398. (In Russ.).

3. Graham R.L., Lawler E.L., Lenstra J.K., Rinnooy Kan A.H.G. Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey. Annals of Discrete Mathematics. 1979;5:287–326. https://doi.org/10.1016/S0167-5060(08)70356-X

4. Gairing M., Lücking T., Mavronicolas M., Monien B. Computing Nash Equilibria for Scheduling on Restricted Parallel Links. In: STOC '04: Proceedings of the thirty-sixth annual ACM symposium on Theory of computing, 13–16 June 2004, Chicago, IL, USA. New York: Association for Computing Machinery; 2004. pp. 613–622. https://doi.org/10.1145/1007352.1007446

5. Chen B., Potts C.N., Woeginger G.J. A Review of Machine Scheduling: Complexity, Algorithms and Approximability. In: Handbook of Combinatorial Optimization. Boston: Springer; 1998. pp. 1493–1641. https://doi.org/10.1007/978-1-4613-0303-9_25

6. Gunawan A., Ng K.M., Poh K.L. Solving the Teacher Assignment-Course Scheduling Problem by a Hybrid Algorithm. International Journal of Computer and Information Engineering. 2007;1(2):137–142.

7. Tanaev V.S., Shkurba V.V. Vvedenie v teoriyu raspisanii. Moscow: Glavnaya redaktsiya fiziko-matematicheskoi literatury izd-va "Nauka"; 1975. 256 p. (In Russ.).

8. Tanaev V.S., Gordon V.S., Shafranskii Ya.M. Teoriya raspisanii. Odnostadiinye sistemy. Moscow: Nauka. Glavnaya redaktsiya fiziko-matematicheskoi literatury; 1984. 384 p. (In Russ.).

9. Lazarev A.A. Models and solution methods for problems in theory of scheduling. Avtomatika i telemekhanika. 2014;(7):14–16. (In Russ.).

10. Batishchev D.I., Gudman E.D., Norenkov I.P., Prilutskii M.Kh. Metod dekompozitsii dlya resheniya kombinatornykh zadach uporyadocheniya i raspredeleniya resursov. Informatsionnye tekhnologii = Information Technologies. 1997;(1):29–33. (In Russ.).

11. Batishchev D.I., Gudman E.D., Norenkov I.P., Prilutskii M.Kh. Metod kombinirovaniya evristik dlya resheniya kombinatornykh zadach uporyadocheniya i raspredeleniya resursov. Informatsionnye tekhnologii = Information Technologies. 1997;(2):29–32. (In Russ.).

12. Prilutsky M.Kh. Distribution of a homogeneous resource in hierarchical systems of a tree-like structure. In: System Identification and Control Problems SICPRO '2000: Proceedings of the International Conference, 26–28 September 2000, Moscow, Russia. Moscow: V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences; 2000. pp. 2038–2049. (In Russ.).

13. Prilutskii M.Kh. Multicriterial multi-index resource scheduling problems. Journal of Computer and Systems Sciences International. 2007;46(1):78–82. https://doi.org/10.1134/S1064230707010091

14. Varlamov O.O., Krivosheev O.V. Application of mivar technologies of logical artificial intelligence to solve problems of resource allocation of production systems. Sistemy upravleniya i informatsionnye tekhnologii. 2022;(1):49–56. (In Russ.). https://doi.org/10.36622/VSTU.2022.87.1.011

15. 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.).

16. Kotsenko A.A., Goryachkin B.S., Bazanova A.G., Marushchenko A.V., Varlamov O.O. Model for describing mivar networks in the format of bipartite and tripartite oriented graphs for decision-making and information processing in machine-building AI. Dinamika slozhnykh sistem – XXI vek = Dynamics of Complex Systems – XXI Century. 2024;18(1):5–17. (In Russ.).

17. Varlamov O.O. Big knowledge: expanding the applications of mivar technologies of logic AI. In: MIVAR'23: Sbornik nauchnykh statei. Moscow: INFRA-M; 2023. pp. 591–597. (In Russ.).

18. Varlamov O., Kotsenko A., Aladin D., Zheltova A., Marushchenko A. Mivar robot decision making systems. RoboMind. Moscow: INFRA-M; 2024. 549 p. (In Russ.).

19. Kotsenko A.A., Kozyrev S.A., Todua D.G., Marushchenko A.V., Varlamov O.O. Research on the application of mivar technologies for planning routes of robotic complexes in three-dimensional logical space. Estestvennye i tekhnicheskie nauki = Natural and Technical Sciences. 2024;(2):190–196. (In Russ.). https://doi.org/10.25633/ETN.2024.02.12

20. Varlamov O., Aladin D. A New Generation of Rules-based Approach: Mivar-based Intelligent Planning of Robot Actions (MIPRA) and Brains for Autonomous Robots. Machine Intelligence Research. 2024. https://doi.org/10.1007/s11633-023-1473-1

21. Varlamov O.O. Preparing initial data for creating mivar knowledge bases decision-making systems of robots. In: MIVAR'23: Sbornik nauchnykh statei. Moscow: INFRA M; 2023. pp. 545–551. (In Russ.).

22. Chestnova E.A., Fedoseeva E.Yu., Vaganov D.D. et al. Development of the MES knowledge base for the selection of dosage forms for antibiotics and antimycotics. Estestvennye i tekhnicheskie nauki = Natural and Technical Sciences. 2023;(5):29–33. (In Russ.). https://doi.org/10.25633/ETN.2023.05.01

23. Zheltova A.A., Varlamov O.O. Complex AI: car sign recognition analysis in photos. In: MIVAR'23: Sbornik nauchnykh statei. Moscow: INFRA M; 2023. pp. 412–417. (In Russ.).

24. Maksimov N.V., Varlamov O.O. Big knowledge: models and tools for representation, search, and processing of knowledge in tasks of intellectual activity. In: MIVAR'23: Sbornik nauchnykh statei. Moscow: INFRA M; 2023. pp. 579–590. (In Russ.).

25. Varlamov O.O. Creating Big Knowledge and expanding the applications of mivar technologies of logical artificial intelligence. Informatsionnye i matematicheskie tekhnologii v nauke i upravlenii = Information and Mathematical Technologies in Science and Management. 2023;(4):30–41. (In Russ.). https://doi.org/10.25729/ESI.2023.32.4.003

26. Bakanov S.V., Osipov V.G., Varlamov O.O. On the application of mivar AI technologies for systems of modeling processes of product life cycle – BPMS. Informatsiya i obrazovanie: granitsy kommunikatsii = Information and Education: Borders of Communications. 2022;(14):227–229. (In Russ.).

27. Volkov A., Varlamov O. Method of creation of a two-level neural network structure for solving problems in mechanical engineering. In: Intelligent Information Technology and Mathematical Modeling 2021 (IITMM 2021): Journal of Physics: Conference Series: Volume 2131, 31 May – 06 June 2021, Gelendzhik, Russia. IOP Publishing; 2021. https://doi.org/10.1088/1742-6596/2131/3/032003

28. Mamatkulov U.B., Kesel S.A., Semenov D.V. et al. Mivar intellectualization of SGRC information security platforms. In: MIVAR'22: Sbornik nauchnykh statei. Moscow: INFRA M; 2022. pp. 269–275. (In Russ.).

29. Varlamov O.O., Krivosheev O.V. The use of mivar networks for resource allocation of production systems. In: MIVAR'22: Sbornik nauchnykh statei. Moscow: INFRA-M; 2022. pp. 376–382. (In Russ.).

30. Varlamov O.O., Krivosheev O.V. Application of a combined algorithm for allocating resources of production systems with incomplete data. In: MIVAR'22: Sbornik nauchnykh statei. Moscow: INFRA-M; 2022. pp. 383–389. (In Russ.).

31. Blokhina S.V. et al. O problemakh obrazovaniya, tselevom obraze "shkoly budushchego", informatizatsii i perspektivnykh informatsionnykh tekhnologiyakh obrazovaniya. Izvestiya YuFU. Tekhnicheskie nauki = Izvestiya SFedU. Engineering Sciences. 2007;(5):195–200. (In Russ.).

32. Podkosova Ya.G., Vasyugova S.A., Varlamov O.O. Novye vozmozhnosti i ogranicheniya tekhnologii virtual'noi real'nosti dlya provedeniya nauchnykh issledovanii, trekhmernoi vizualizatsii rezul'tatov modelirovaniya i sozdaniya mivarnykh obuchayushchikh sistem i trenazherov. Trudy Nauchno-issledovatel'skogo instituta radio. 2011;(2):13–23. (In Russ.).

33. Adamova L.E., Varlamov O.O. Ensuring the psychological safety of students during the pandemic and digitalization. In: MIVAR'22: Sbornik nauchnykh statei. Moscow: INFRA M; 2022. pp. 315–322. (In Russ.).

34. Terekhov V.I., Goryachkin B.S. Development of relevant scientific areas as a continuation of the scientific schools of the department "Information processing and management systems" of The Bauman Moscow State Technical University. Dinamika slozhnykh sistem – XXI vek = Dynamics of Complex Systems – XXI Century. 2023;17(3):25–33. (In Russ.).

35. Zhang S., Pravdina A.D., Timofeev V.B., Varlamov O.O. MES for planning workshop resources and analyzing deviations. In: Mezhdunarodnaya Nauchnaya Konferentsiya Molodezhnoi Shkoly "MIVAR'24": MIVAR'24: Sbornik nauchnykh statei, 18–20 April 2024, Moscow, Russia. Moscow: INFRA-M; 2024. pp. 112–117. (In Russ.).