Designing a multi-agent system for a network enterprise
Abstract
The necessity to enhance the efficiency of modern network enterprises based on digital platform technologies, Digital Twins, and Digital Threads determines the relevance of implementing dynamic multi-agent technologies in production practice. The architectural complexity of existing multi-agent systems (MAS) and the lack of scientific research in the field of justifying methods and tools for their creation motivate the goal of this study to develop a comprehensive MAS design technology. This technology should encompass all architectural levels and allow for the adaptation of reference and best design practices. This article analyzes the possibilities of applying Digital Twins and Digital Threads in the creation of network enterprises and proposes methods for their implementation using MAS. A design technology for MAS has been developed in accordance with the IIRA (Industrial Internet Reference Architecture) and RAMI (Reference Architectural Model Industrie 4.0) architectural frameworks, which enables the interconnected formation and display of design results across various architectural levels. At the business level, a method is proposed for formulating business requirements for MAS based on the selection and adaptation of business models and application scenarios. At the level of constructing manufacturing and business processes, a method for formulating functional requirements for MAS is presented, revealing the transition from value networks to manufacturing and business process structures. At the level of functional design of the network enterprise’s multi-agent system, a method is proposed for forming key design solutions from the perspective of implementing various service categories using AAS (Asset Administrative Shells) and their specialization. At the technological implementation design level of MAS, a method for implementing software agents using a microservice software organization is proposed. The method presented for adapting reference and best MAS design models allows for the selection of appropriate design solutions from libraries of reference models and knowledge bases for subsequent refinement. This accelerates and improves the quality of the design process. The implementation of the developed technology for designing multi-agent systems will increase the adaptability of network enterprises to dynamically changing business needs, taking into account the interests and capabilities of all stakeholders.
Downloads
References
Matthyssens P. (2019) Reconceptualizing value innovation for Industry 4.0 and the Industrial Internet of Things. Journal of Business & Industrial Marketing, vol. 34, no. 6, pp. 1203–1209. https://doi.org/10.1108/JBIM-11-2018-0348
Feofanov A.N. Bondarchuk E.Yu., Tyasto S.A. (2018) Organization of a virtual enterprise – the future of production. Bulletin of MSTU "Stankin", no. 3 (46), pp. 101–105 (in Russian).
Müller J.M. (2019) Antecedents to digital platform usage in Industry 4.0 by established manufacturers. Sustainability, vol. 11, no. 4, article 1121. https://doi.org/10.3390/su11041121
Golovin S.A., Lotsmanov A.N., Pozdneev B.M (2021) The Russian Federation Industry 4.0 program is a chance not to fall behind forever in the field of industrial production. World of Information Technologies, no. 1–2, pp. 38–40 (in Russian).
Borovkov A.I., Prokhorov A., Lysachev M. (2020) Digital twin. Analysis, trends, world experience. Moscow: Alliance Print (in Russian).
Rosstandart (2021) National Standard of the Russian Federation GOST R 57700.37–2021. Computer models and simulation. Digital twins of products. General provisions (in Russian).
National Institute of Standards and Technology (2018) Digital thread for smart manufacturing. Available at: https://www.nist.gov/programs-projects/digital-thread-smart-manufacturing (accessed 1 August 2024).
Makarov V.L., Bakhtizin A.R., Beklaryan G.L. (2019) Developing digital twins for production enterprises. Business Informatics, vol. 14, no. 1, pp. 7–16. http://doi.org/10.17323/1998-0663.2019.4.7.16
Makarov V.L., Bakhtizin A.R., Beklaryan G.L., Akopov A.S. (2021) Digital plant: methods of discrete-event modeling and optimization of production characteristics. Business Informatics, vol. 15, no. 2, pp. 7–20. http://doi.org/10.17323/2587-814X.2021.2.7.20
Gorodetsky V.I. (2019) Behavioral models of cyberphysical systems and group management. Basic concepts. News of the Southern Federal University. Technical Sciences, no. 1 (203), pp. 144–162.
Corsini R.R., Costa A., Fichera S., Framinan J.M. (2024) Digital twin model with machine learning and optimization for resilient production–distribution systems under disruptions. Computers & Industrial Engineering, vol. 191, article 110145.
Kabaldin Yu.G., Shatagin D.A., Anosov M.S., Kuzmishina A.M. (2019) Development of digital twin of CNC unit based on machine learning methods. Vestnik of Don State Technical University, vol. 19, no. 1, pp. 45–55 (in Russian). https://doi.org/10.23947/1992-5980-2019-19-1-45-55
Skobelev P., Mayorov I., Simonova E., Goryanin O., Zhilyaev A., Tabachinskiy A., Yalovenko V. (2020) Development of models and methods for creating a digital twin of plants within the cyber-physical system for precision farming management. Journal of Physics: Conference Series, vol. 1703, pp. 12–22. https://doi.org/10.1088/1742-6596/1703/1/012022
Industry IoT Consortium (2022) The industrial internet reference architecture. Available at: https://www.iiconsortium.org/wp-content/uploads/sites/2/2022/11/IIRA-v1.10.pdf (accessed 1 August 2024).
Plattform Industrie 4.0 (2018) Plattform Industry 4.0. Reference architectural model Industry 4.0 (RAMI4.0) – An introduction. Available at: https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/rami40-an-introduction.html (accessed 1 August 2024).
Seitz M., Gehlhoff F., Cruz Salazar L.A., Fay A., Vogel-Heuser B. (2021) Automation platform independent multi-agent system for robust networks of production resources in industry 4.0. Journal of Intelligent Manufacturing, vol. 32, pp. 2023–2041.
Karnouskos S., Leitao P., Ribeiro L., Colombo A.W. (2020) Industrial agents as a key enabler for realizing industrial cyber-physical systems: Multiagent systems entering Industry 4.0. IEEE Industrial Electronics Magazine, vol. 14, no. 3, pp. 18–32. https://doi.org/10.1109/MIE.2019.2962225
Vogel-Heuser B., Ocker F., Scheuer T. (2021) An approach for leveraging Digital Twins in agent-based production systems. at – Automatisierungstechnik, vol. 69, no. 12, pp. 1026–1039. https://doi.org/10.1515/auto-2021-0081
Telnov Yu.F., Kazakov V.A., Danilov A.V., Denisov A.A. (2022). Requirements for the software implementation of the Industrie 4.0 system for creating network enterprises. Software & Systems, vol. 35, no. 4, pp. 557–571 (in Russian).
Rosstandart (2022) National Standard of the Russian Federation GOST R 70265.1–2022. Industrial-process measurement, control and automation. Digital factory framework. Part 1. Basic provisions (in Russian).
Plattform Industrie 4.0 (2019) Discussion Paper: Usage View of the Asset Administration Shell. Available at: https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/2019-usage-view-asset-administration-shell.html (accessed 1 August 2024).
Telnov Yu.F., Kazakov V.A., Bryzgalov A.A., Fiodorov I.G. (2023) Methods and models for substantiating application scenarios for the digitalization of manufacturing and business processes of network enterprises. Business Informatics, vol. 17, no. 4, pp. 73–93. http://doi.org/10.17323/2587-814X.2023.4.73.93
Segovia M., Garcia-Alfaro J. (2022) Design, modeling and implementation of digital twins. Sensors, vol. 22, no. 14, article 5396. https://doi.org/10.3390/s22145396
Bajaj M., Hedberg T. (2018) System lifecycle handler – Spinning a digital thread for manufacturing. INCOSE International Symposium, vol. 28, no. 1, pp. 1636–1650. https://doi.org/10.1002/j.2334-5837.2018.00573.x
Idaho National Laboratory (2020) Deep-Lynx. Available at: https://github.com/idaholab/Deep-Lynx (accessed 1 August 2024).
Bonham E., McMaster K., Thomson E., Panarotto M., Müller J.R., Isaksson O., Johansson E. (2020) Designing and integrating a digital thread system for customized additive manufacturing in multi-partner kayak production. Systems, vol. 8, no. 4, article 43. https://doi.org/10.3390/systems8040043
Tarassov V.B. (2019) Enterprise total agentification as a way to Industry 4.0: Forming artificial societies via goal-resource networks. Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). Advances in Intelligent Systems and Computing (AISC), vol. 1156, pp. 26–40.
Sakurada L., Leitao P., de la Prieta F. (2022) Agent-based asset administration shell approach for digitizing industrial assets. IFAC-PapersOnLine, vol. 55, no. 2, pp. 193–198.
Spanoudakis N.I., Moraitis P. (2007) The agent systems methodology (ASEME): A preliminary report. Computer Science.
Julian V., Botti V. (2004) Developing real-time multi-agent system. Integrated Computer-Aided Engineering, vol. 11, no. 2, pp. 135–149. https://doi.org /10.3233/ICA-2004-11204
Eleftherakis G., Kefalas P., Kehris E. (2011) A methodology for developing component-based agent focusing systems on component quality. Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS 2011), Szczecin, Poland, 18–21 September 2011, pp. 561–568.
Plattform Industrie 4.0 (2020) Digital Twin and Asset Administration Shell Concepts and Application in the Industrial Internet and Industrie 4.0. Available at: https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/Digital-Twin-and-Asset-Administration-Shell-Concepts.pdf (accessed 1 August 2024).
Foundation for Intelligent Physical Agents (2002) FIPA ACL message structure specification. Available at: http://www.fipa.org/specs/fipa00061/SC00061G.pdf (accessed 1 August 2024).
Plattform Industrie 4.0 (2021) Functional view of the asset administration shell in an Industrie 4.0 system environment. Available at: https://www.plattform-i40.de/IP/Redaktion/DE/Downloads/Publikation/Functional-View.html (accessed 1 August 2024).
Lewis J., Fowler M. (2014) Microservices. A definition of this new architectural term. Available at: https://martinfowler.com/articles/microservices.html (accessed 1 August 2024).
Richardson C. (2018) Microservices Patterns: With examples in Java. Manning Publications.
Plattform Industrie 4.0 (2017) Exemplification of the Industrie 4.0 application scenario value-based service following IIRA structure. Available at: https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/exemplification-i40-value-based-service.pdf (accessed 1 August 2024).
Gassmann O., Csik M., Frankenberg K. (2014) The business model navigator: 55 models that will revolutionise your business. FT Press.
Telnov Yu.F., Bryzgalov A.A., Kozyrev P.A., Koroleva D.S. (2022) Choosing the type of business model to implement the digital transformation strategy of a network enterprise. Business Informatics, vol. 16, no. 4, pp. 50–67. http://doi.org/10.17323/2587-814X.2022.4.50.67
Plattform Industrie 4.0 (2018) Usage viewpoint of application scenario value-based service. Available at: https://www.plattform-i40.de/I40/Redaktion/DE/Downloads/Publikation/hm-2018-usage-viewpoint.html (accessed 1 August 2024).
Rosstandart (2021) National Standard of the Russian Federation GOST R 59799–2021. Smart manufacturing. Reference architecture model industry 4.0 (RAMI 4.0) (in Russian).
Plattform Industrie 4.0 (2022) Details of the asset administration shell – Part 1. The exchange of information between partners in the value chain of Industrie 4.0. Available at: https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/Details_of_the_Asset_Administration_Shell_Part1_V3.html (accessed 1 August 2024).
Telnov Yu.F., Kazakov V.A., Danilov A.V., Bryzgalov A.A. (2023) Network enterprises: Production and business process models based on multi-agent systems. Software & Systems, vol. 36, no. 4, pp. 632–643.
Kolodner J. (1993) Case-based reasoning. Morgan Kaufmann.
.jpg)
