Abstract
In this paper we present a dynamic multiple criteria model of integrated adaptive planning and scheduling for complex objects (CO). Various types of CO are in use currently, for example: virtual enterprises, supply chains, telecommunication systems, etc. Hereafter, we refer to CO as systems of those types. The adaptation control loops are explicitly integrated within the model of analytical simulation. The mathematical approach is based on a combined application of control theory, operations research, systems analysis, and modeling and simulation theory. In particular, a scheduling problem for CO is considered as a dynamic interpretation. New procedures of dynamic decomposition help us to find the parameter values of the model’s adaptation. The example demonstrates a general optimization scheme to be applied to the problem of division of competencies between the coordinating and operating levels of the CO via parametric adaptation of the model’s described structure dynamics control processes.
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Ohtilev, M.Y., Sokolov, B.V., Yusupov, R.M.: Intellectual Technologies for Monitoring and Control of Structure dynamics of Complex Technical Objects, 410 p. Moscow, Nauka (2006) (in Russian)
Zaychik, E., Sokolov, B., Verzilin, D.: Integrated modeling of structure dynamics control in complex technical systems. In: 19th European Conference on Modeling and Simulation ESMS 2005, “Simulation in Wider Europe”, June 1-4, pp. 341–346. Riga Technical University, Riga (2005)
Ivanov, D., Sokolov, B., Arkhipov, A.: Stability analysis in the Framework of decision Making under Risk and Uncertainty Network – Centric Collaboration and Supporting Frameworks. In: Camarinha-Matos, L., Afsarmanesh, H., OUus, M. (eds.) Network – Centric Collaboration and Supporting Frameworks, IFIP TC5WG 5.5 Seventh IFIP Working Conference on Virtual Enterprises, Helsinki, Finland, September 25-27. IFIP, vol. 224, pp. 211–218. Springer, Boston (2006)
Skurihin, V.I., Zabrodsky, V.A., Kopeychenko, Y.V.: Adaptive control systems in machine-building industry. Mashinostroenie, – M. (1989)
Rastrigin, L.A.: Modern principles of control for complicated objects. Sovetscoe Radio, – M. (1980) (in Russian)
Bellmann, R.: Adaptive Control Processes: A Guided Tour. Princeton Univ. Press, Princeton (1972)
Rastrigin, L.A.: Adaptation of complex systems. Zinatne, Riga (1981) (in Russian)
Fleming, W.H., Richel, R.W.: Deterministic and stochastic optimal control. Springer, New York (1975)
Moiseev, N.N.: Element of the Optimal Systems Theory. Nauka, – M. (1974) (in Russian)
Sowa, J.: Architecture for intelligent system. IBM System Journal 41(3) (2002)
Zypkin Ya. Z. Adaptation and teachning in automatic systems. Nauka, – M. (1969) (in Russian)
Bryson, A.E., Ho, Y.-C.: Applied optimal control: Optimization, Estimation and Control. Waltham Massachusetts, Toronto, London (1969)
Chernousko, F.L., Zak, V.L.: On Differential Games of Evasion from Many Pursuers. J. Optimiz. Theory and Appl. 46(4), 461–470 (1985)
Singh, M., Titli, A.: Systems: Decomposition, Optimization and Control. Pergamon Press, Oxford (1978)
Petrosjan, L.A., Zenkevich, N.A.: Game Theory. World Scientific Publ., Singapore (1996)
Roy, B.: Multi-criteria Methodology for Decision Aiding. Kluwer Academic Pulisher, Dordrecht (1996)
Fischer, M., Jaehn, H., Teich, T.: Optimizing the selection of partners in production networks. Robotics and Computer-Integrated Manufacturing 20, 593–601 (2004)
Huang, G., Zhang, Y., Liang, L.: Towards integrated optimal configuration of platform products, manufacturing processes, and supply chains. Journal of Operations Management 23, 267–290 (2005)
Kuehnle, H.: A system of models contribution to production network (PN) theory. Journal of Intelligent Manufacturing, 157–162 (2007)
Nilsson, F., Darley, V.: On complex adaptive systems and agent-based modeling for improving decision-making in manufacturing and logistics settings. Int. Journal of Operations and Production Management 26(12), 1351–1373 (2006)
Rabelo, R.J., Klen, A.A.P., Klen, E.R.: Multi-agent system for smart coordination of dynamic supply chains. In: Proceedings of the 3rd International Conference on Virtual Enterprises, PRO-VE, pp. 379–387 (2002)
Teich, T.: Extended Value Chain Management (EVCM). GUC-Verlag, Chemnitz (2003)
Wu, N., Mao, N., Qian, Y.: An approach to partner selection in agile manufacturing. Journal of Intelligent Manufacturing 10(6), 519–529 (1999)
Wu, N., Su, P.: Selection of partners in virtual enterprise paradigm. Robotics and Computer-Integrated Manufacturing 21, 119–131 (2005)
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Sokolov, B.V., Zelentsov, V.A., Brovkina, O., Mochalov, V.F., Potryasaev, S.A. (2015). Models Adaptation of Complex Objects Structure Dynamics Control. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Intelligent Systems in Cybernetics and Automation Theory. CSOC 2015. Advances in Intelligent Systems and Computing, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-319-18503-3_3
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DOI: https://doi.org/10.1007/978-3-319-18503-3_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18502-6
Online ISBN: 978-3-319-18503-3
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