A Multi-Model Approach for Simulation-Based Digital Twin in Resilient Services

Complex cyber-physical systems demand integrated solution approaches. The current work presents a multi-model approach for simulation-based digital twins as a formal and technological foundation for the analysis and improvement of resilient services. The given approach has several significant benefits including the possibility to conduct interactive simulations and experiments based on systems engineering principles, to share data across multiple data sources and storages, to manage operations in real-time, as well as to enable collaboration between the users in an integrated web platform. The proposal is illustrated on the use cases of secure telemedicine services and secure remote workplace. Key-Words: Multi-Modelling, Simulation, Digital Twin, Resilient Services Received: December 15, 2020. Revised: January 20, 2021. Accepted: January 22, 2021. Published: January 29, 2021.

[1]  Bengt Lennartson,et al.  Formal Properties of the Digital Twin – Implications for Learning, optimization, and Control , 2020, 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE).

[2]  Huiyue Dong,et al.  Review of digital twin about concepts, technologies, and industrial applications , 2020 .

[3]  Kurt Sandkuhl,et al.  Supporting Early Phases of Digital Twin Development with Enterprise Modeling and Capability Management: Requirements from Two Industrial Cases , 2020, BPMDS/EMMSAD@CAiSE.

[4]  Louise Wright,et al.  How to tell the difference between a model and a digital twin , 2020, Advanced Modeling and Simulation in Engineering Sciences.

[5]  Souvik Barat,et al.  An Actor Based Simulation Driven Digital Twin For Analyzing Complex Business Systems , 2019, 2019 Winter Simulation Conference (WSC).

[6]  K. Kim.  At the End of the Year 2019 , 2019, International Neurourology Journal.

[7]  Souvik Barat,et al.  Towards Adaptive Enterprises Using Digital Twins , 2019, 2019 Winter Simulation Conference (WSC).

[8]  Andrew Y. C. Nee,et al.  Enabling technologies and tools for digital twin , 2019 .

[9]  Bernard P. Zeigler,et al.  Theory of Modeling and Simulation: Discrete Event & Iterative System Computational Foundations , 2018 .

[10]  Arturs Aboltins,et al.  Selection and performance analysis of chaotic spreading sequences for DS-CDMA systems , 2016, 2016 Advances in Wireless and Optical Communications (RTUWO).

[11]  John D. Sterman,et al.  System Dynamics: Systems Thinking and Modeling for a Complex World , 2002 .

[12]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .

[13]  Michael W. Grieves.  Virtually Intelligent Product Systems: Digital and Physical Twins , 2019, Complex Systems Engineering: Theory and Practice.

[14]  K. Vichova,et al.  The impact of crisis situations to the transport service of the territory for the selected hospital , 2019 .

[15]  Janis Stirna,et al.  Capability Management in Digital Enterprises , 2018, Springer International Publishing.

[16]  I. Baronak,et al.  Complex mathematical model of the contact center with determining of the optimal number of agents , 2018 .

[17]  J. Koziorek,et al.  Automated control system design with model-based commissioning , 2018 .

[18]  David Masad,et al.  Mesa: An Agent-Based Modeling Framework , 2015, SciPy.

[19]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .

[20]  Automation systems and integration. Digital twin framework for manufacturing , 2022 .