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
References:
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
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
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
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
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
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
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
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
Kuhn T. Digitaler Zwilling. Informatik-Spektrum, 2017, vol. 40, pp. 440–444. URL: Link
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
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
Lopatin A.S. [Simulated annealing method]. Stokhasticheskaya optimizatsiya v informatike, 2005, vol. 1, no. 1, pp. 133–149. URL: Link (In Russ.)
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
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
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
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.)
Szu H., Hartley R. Fast Simulated Annealing. Physics Letters A, 1987, vol. 122, iss. 3-4, pp. 157–162. URL: Link90796-1
Ingber L. Very Fast Simulated Re-Annealing. Mathematical and Computer Modelling, 1989, vol. 12, iss. 8, pp. 967–973. URL: Link90202-1
Yao X. A New Simulated Annealing Algorithm. International Journal of Computer Mathematics, 1995, vol. 56, iss. 3-4, pp. 161–168. URL: Link
Ingber L. Simulated Annealing: Practice versus Theory. Mathematical and Computer Modelling, 1993, vol. 18, iss. 11, pp. 29–57. URL: Link90204-C
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
Damodaran A. Investment Valuation: Tools and Techniques for Determining the Value of Any Asset. New York, John Wiley & Sons, Inc., 2002, 992 p.