Aspects of program control over technological innovations with consideration of risks
Keywords:program control, technological innovations, dynamic system, minimax result, reachability region
The dynamic system of control of technological innovations is considered. Its dynamics is described by a vector linear discrete recurrent ratio and influenced by control parameters (controls) and an uncontrollable parameter (vector of risks or obstacles). In this case, the risks in the system of control of technological innovation will imply factors that influence negatively or catastrophically the results the processes, considered in it.
To solve the problem on control over technological innovations, we proposed methods based on the construction of predictive sets – reachability regions of the considered dynamic model. These are the sets of all permissible states of a phase vector of the system at an assigned moment, correspondent to the fixed program control and to all permissible vectors of risk. This procedure is accompanied by the minimax-based method for finding a guaranteed result. Its essence is that the value of the worst (maximum) vector of possible risks is the least compared with similar values for the others at minimally guaranteed optimal control. Thus, we minimize the impact of risks in the problem of control of technological innovations, where the risks are uncontrollable parameters. This is implemented based on selection of such optimal control, which would guarantee the obtained result under the influence of any maximal risk from the set of permissible risks.
The proposed method enables the development of effective numerical procedures that make it possible to implement computer modeling of dynamics of the studied problem, to form program minimax control over technological innovations and to obtain an optimal guaranteed result.
The results reported here could be used for economic-mathematical modeling and for solving other problems on the optimization of data forecasting and control processes under conditions of insufficient information and in the existence of risks. In addition, the developed modeling toolset could form the basis for development of appropriate software-hardware complexes to support making effective control decisions in the innovation activity.
Zaslavski, A. J. (2013). Nonconvex Optimal Control and Variational Problems. Springer, 378. doi: 10.1007/978-1-4614-7378-7
Romanko, O., Ghaffari-Hadigheh, A., Terlaky, T. (2012). Multiobjective Optimization via Parametric Optimization: Models, Algorithms, and Applications. Springer Proceedings in Mathematics & Statistics, 77–119. doi: 10.1007/978-1-4614-3924-0_5
Poe, W. A., Mokhatab, S. (2017). Modeling, Control and Optimization of Natural Gas Processing Plant. Amsterdam: Gulf Professional Publishing, 300. doi: 10.1016/c2014-0-03765-3
Pour, F. K., Puig, V., Ocampo-Martinez, C. (2018). Multi-layer health-aware economic predictive control of a pasteurization pilot plant. International Journal of Applied Mathematics and Computer Science, 28 (1). doi: 10.2478/amcs-2018-0007
Madaeni, S. S., Shiri, M., Kurdian, A. R. (2014). Modeling, optimization, and control of reverse osmosis water treatment in kazeroon power plant using neural network. Chemical Engineering Communications, 202 (1), 6–14. doi: 10.1080/00986445.2013.828606
Gontareva, I. V. (2011). Influence of timeliness in reproduction processes upon system efficiency of enterprise development. Aktualni problemy ekonomiky, 2 (116), 69–76.
Goncharuk, A. G. (2009). Enterprise performance management: a three-level approach. International Journal of Business Performance and Supply Chain Modelling, 1 (2/3), 162. doi: 10.1504/ijbpscm.2009.030640
Dyhta, V. A., Samsonyuk, O. N. (1997). Princip maksimuma dlya impul'snyh processov pri ogranicheniyah na obraz i polnuyu variaciyu upravlyayushchey mery. Kraevye zadachi, 122–138.
Bulaev, V. V., Shorikov, A. F. (2018). Discretization Procedure for Linear Dynamical Systems. Journal of Mathematical Sciences, 230 (5), 664–667. doi: 10.1007/s10958-018-3765-5
Bellman, R., Kalaba, R.; Razumihin, B. S. (Ed.) (1969). Dinamicheskoe programmirovanie i sovremennaya teoriya upravleniya. Moscow: Nauka, 118.
Pontryagin, L. S. (2004). Princip maksimuma v optimal'nom upravlenii. Moscow, 64.
Golden, B. L., Wasil, E. A., Harker, P. T. (Eds.) (1989). The Analytic Hierarchy Process. Application and Studies. New York: Springer-Verlag. doi: 10.1007/978-3-642-50244-6
Vasylieva, N. (2016). Cluster models of households’ agrarian production development. Economic Annals-ХХI, 158 (3-4 (2)), 13–16. doi: 10.21003/ea.v158-03
Malyarets, L., Draskovic, M., Babenko, V., Kochuyeva, Z., Dorokhov, O. (2017). Theory and practice of controlling at enterprises in international business. Economic Annals-ХХI, 165 (5-6), 90–96. doi: 10.21003/ea.v165-19
Rafalski, R. (2012). A New Concept of Evaluation of the Production Assets. Foundations of Management, 4 (1). doi: 10.2478/fman-2013-0005
Kaplan, S. (Ed.) (2012). The Business Model Innovation Factory: How to Stay Relevant When The World is Changing. Wiley. doi: 10.1002/9781119205234
Rosegger, G. (1980). The Economics of Production and Innovation. An Industrial Perspective. Oxford, Pergamon Press, 404.
Sosna, M., Trevinyo-Rodríguez, R. N., Velamuri, S. R. (2010). Business Model Innovation through Trial-and-Error Learning. Long Range Planning, 43 (2-3), 383–407. doi: 10.1016/j.lrp.2010.02.003
Teece, D. J. (2010). Business Models, Business Strategy and Innovation. Long Range Planning, 43 (2-3), 172–194. doi: 10.1016/j.lrp.2009.07.003
Babenko, V. A. (2013). Formation of economic-mathematical model for process dynamics of innovative technologies management at agroindustrial enterprises. Actual Problems of Economics, 1 (139), 182–186.
Babenko, V., Romanenkov, Y., Yakymova, L., Nakisko, A. (2017). Development of the model of minimax adaptive management of innovative processes at an enterprise with consideration of risks. Eastern-European Journal of Enterprise Technologies, 5 (4 (89)), 49–56. doi: 10.15587/1729-4061.2017.112076
Babenko, V., Chebanova, N., Ryzhikova, N., Rudenko, S., Birchenko, N. (2018). Research into the process of multi-level management of enterprise production activities with taking risks into consideration. Eastern-European Journal of Enterprise Technologies, 1 (3 (91)), 4–12. doi: 10.15587/1729-4061.2018.123461
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Copyright (c) 2018 Vitalina Babenko, Oleksandr Nazarenko, Inna Nazarenko, Oleksandra Mandych, Marharyta Krutko
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