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Autonomous Digital Twin of Enterprise: Method and Toolset for Knowledge-Based Multi-Agent Adaptive Management of Tasks and Resources in Real Time

Author

Listed:
  • Vladimir Galuzin

    (Information Technology Faculty, Samara State Technical University, Molodogvardeyskaya Str. 244, 443100 Samara, Russia
    Knowledge Genesis Group, Skolkovo, Bolshoy Bulv. 42, 121205 Moscow, Russia)

  • Anastasia Galitskaya

    (Knowledge Genesis Group, Skolkovo, Bolshoy Bulv. 42, 121205 Moscow, Russia)

  • Sergey Grachev

    (Information Technology Faculty, Samara State Technical University, Molodogvardeyskaya Str. 244, 443100 Samara, Russia
    Knowledge Genesis Group, Skolkovo, Bolshoy Bulv. 42, 121205 Moscow, Russia)

  • Vladimir Larukhin

    (Information Technology Faculty, Samara State Technical University, Molodogvardeyskaya Str. 244, 443100 Samara, Russia
    Knowledge Genesis Group, Skolkovo, Bolshoy Bulv. 42, 121205 Moscow, Russia
    Samara Federal Center of Russian Academy of Science, Studenchesky Str., 3A, 443001 Samara, Russia)

  • Dmitry Novichkov

    (Information Technology Faculty, Samara State Technical University, Molodogvardeyskaya Str. 244, 443100 Samara, Russia)

  • Petr Skobelev

    (Samara Federal Center of Russian Academy of Science, Studenchesky Str., 3A, 443001 Samara, Russia)

  • Alexey Zhilyaev

    (Knowledge Genesis Group, Skolkovo, Bolshoy Bulv. 42, 121205 Moscow, Russia)

Abstract

Digital twins of complex technical objects are widely applied for various domains, rapidly becoming smart, cognitive and autonomous. However, the problem of digital twins for autonomous management of enterprise resources is still not fully researched. In this paper, an autonomous digital twin of enterprise is introduced to provide knowledge-based multi-agent adaptive allocation, scheduling, optimization, monitoring and control of tasks and resources in real time, synchronized with employees’ plans, preferences and competencies via mobile devices. The main requirements for adaptive resource management are analyzed. The authors propose formalized ontological and multi-agent models for developing the autonomous digital twin of enterprise. A method and software toolset for designing the autonomous digital twin of enterprise, applicable for both operational management of tasks and resources and what-if simulations, are developed. The validation of developed methods and toolsets for IT service desk has proved increase in efficiency, as well as savings in time and costs of deliveries for various applications. The paper also outlines a plan for future research, as well as a number of new potential business applications.

Suggested Citation

  • Vladimir Galuzin & Anastasia Galitskaya & Sergey Grachev & Vladimir Larukhin & Dmitry Novichkov & Petr Skobelev & Alexey Zhilyaev, 2022. "Autonomous Digital Twin of Enterprise: Method and Toolset for Knowledge-Based Multi-Agent Adaptive Management of Tasks and Resources in Real Time," Mathematics, MDPI, vol. 10(10), pages 1-27, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1662-:d:814491
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    References listed on IDEAS

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    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Alexander A. Lazarev & Nikolay Pravdivets & Frank Werner, 2020. "On the Dual and Inverse Problems of Scheduling Jobs to Minimize the Maximum Penalty," Mathematics, MDPI, vol. 8(7), pages 1-15, July.
    3. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331, October.
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