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Financial Analytics: Science and Experience

Developing the digital twin of the economic, financial, information and logistics inter-cluster cooperation mechanism

Vol. 16, Iss. 3, SEPTEMBER 2023

Received: 1 June 2020

Received in revised form: 15 June 2020

Accepted: 29 June 2020

Available online: 30 August 2023


JEL Classification: C63, E17, O21, O36

Pages: 301–320


Sergei N. YASHIN National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation


Yurii V. TRIFONOV National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation


Egor V. KOSHELEV National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation


Subject. This article deals with the issues related to the use of digital twins in order to manage innovation and industrial clusters and the liaison between them.
Objectives. The article aims to develop a digital twin model of inter-cluster cooperation within a Federal district of Russia. The Volga (Privolzhsky) Federal District is considered a case study.
Methods. For the study, we used a multiple non-linear regression method and a fast simulated annealing (FSA).
Results. The article offers and describes a designed digital twin model of inter-cluster cooperation mechanism.
Conclusions. When reallocating investment and human resources within one federal district, the interests of the population of innovation and industrial clusters should be taken into account, as only just an increase in fixed investment does not always lead to the growth of the region's population. The use of the digital twin model of inter-cluster cooperation mechanism will help avoid premature unreasonable management decisions of the public-policy level regarding the further development of innovation-industrial clusters.

Keywords: digital twin, inter-cluster cooperation


  1. Yashin S.N., Koshelev E.V., Kostrigin R.V. [Compilation of linear functional of the value of the innovation and industrial cluster for the region]. Upravlenie ekonomicheskimi sistemami: elektronnyi nauchnyi zhurnal, 2019, no. 12. (In Russ.) URL: Link
  2. Uhlemann T.H.-J., Schock C., Lehmann C. et al. The Digital Twin. Demonstrating the Potential of Real Time Data Acquisition in Production Systems. Procedia Manufacturing, 2017, no. 9, pp. 113–120. URL: Link
  3. Negri E., Fumagalli L., Macchi M. A Review of the Roles of Digital Twin in CPS-based Production Systems. Procedia Manufacturing, 2017, vol. 11, pp. 939–948. URL: Link
  4. Lee J., Bagheri B., Kao H.-A. A Cyber-Physical Systems Architecture for Industry 4.0-based Manufacturing Systems. Manufacturing Letters, 2015, no. 3, pp. 18–23. URL: Link
  5. Boschert S., Rosen R. Digital Twin – The Simulation Aspect. In: Hehenberger P., Bradley D. (eds) Mechatronic Futures. Cham, Springer International Publishing, 2016, pp. 59–74. URL: Link
  6. Tao F., Cheng J., Qi Q. et al. Digital Twin-driven Product Design, Manufacturing and Service with Big Data. The International Journal of Advanced Manufacturing Technology, 2018, vol. 94, pp. 3563–3576. URL: Link
  7. Rosen R., von Wichert G., Lo G., Bettenhausen K.D. About the Importance of Autonomy and Digital Twins for the Future of Manufacturing. IFAC-PapersOnLine, 2015, vol. 48, iss. 3, pp. 567–572. URL: Link
  8. Grieves M., Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In: Kahlen F.-J., Flumerfelt S., Alves A. (eds) Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, Cham, Springer International Publishing, 2017, pp. 85–113. URL: Link
  9. Kuhn T. Digitaler Zwilling. Informatik-Spektrum, 2017, vol. 40, pp. 440–444. URL: Link
  10. Garetti M., Rosa P., Terzi S. Life Cycle Simulation for the Design of Product-Service Systems. Computers in Industry, 2012, vol. 63, iss. 4, pp. 361–369. URL: Link
  11. Lee J., Lapira E., Bagheri B., Kao H.-A. Recent Advances and Trends in Predictive Manufacturing Systems in Big Data Environment. Manufacturing Letters, 2013, vol. 1, iss. 1, pp. 38–41. URL: Link
  12. Lopatin A.S. [Simulated annealing method]. Stokhasticheskaya optimizatsiya v informatike, 2005, vol. 1, no. 1, pp. 133–149. URL: Link (In Russ.)
  13. Ingber L., Rosen B. Genetic Algorithms and Very Fast Simulated Reannealing: A Comparison. Mathematical and Computer Modelling, 1992, vol. 16, iss. 11, pp. 87–100. URL: Link90108-W
  14. Kirkpatrick S., Gelatt C.D. Jr., Vecchi M.P. Optimization by Simulated Annealing. Readings in Computer Vision. Issues, Problem, Principles, and Paradigms, 1987, pp. 606–615. URL: Link
  15. Metropolis N., Rosenbluth A.W., Rosenbluth M.N. et al. Equation of State Calculations by Fast Computer Machines. The Journal of Chemical Physics, 1953, vol. 21, iss. 6, pp. 1087–1092. URL: Link
  16. Tikhomirov A.S. [About the fast versions of annealing algorithm (Simulated Annealing)]. Stokhasticheskaya optimizatsiya v informatike, 2009, vol. 5, no. 1, pp. 65–90. URL: Link (In Russ.)
  17. Szu H., Hartley R. Fast Simulated Annealing. Physics Letters A, 1987, vol. 122, iss. 3-4, pp. 157–162. URL: Link90796-1
  18. Ingber L. Very Fast Simulated Re-Annealing. Mathematical and Computer Modelling, 1989, vol. 12, iss. 8, pp. 967–973. URL: Link90202-1
  19. Yao X. A New Simulated Annealing Algorithm. International Journal of Computer Mathematics, 1995, vol. 56, iss. 3-4, pp. 161–168. URL: Link
  20. Ingber L. Simulated Annealing: Practice versus Theory. Mathematical and Computer Modelling, 1993, vol. 18, iss. 11, pp. 29–57. URL: Link90204-C
  21. Ingber L. Adaptive Simulated Annealing (ASA): Lessons Learned. Control and Cybernetics, 1996, vol. 25, no. 1, pp. 33–54. URL: Link_ annealing_ASA_Lessons_learned
  22. Damodaran A. Investment Valuation: Tools and Techniques for Determining the Value of Any Asset. New York, John Wiley & Sons, Inc., 2002, 992 p.

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