Vladimir Fedorovich Belov (МГУ им. Н.П. Огарева/АУ «Технопарк–Мордовия») :
Articles:
004.942 Modelling of industrial environment with the help of discrete numerical algorithms
Belov V. F. (МГУ им. Н.П. Огарева/АУ «Технопарк–Мордовия»), Gavryushin S. S. (Bauman Moscow State Technical University), Markova Y. N. (АУ «Технопарк–Мордовия»), Zankin A. I. (МГУ им. Н.П. Огарева)
doi: 10.18698/2309-3684-2022-1-109128
Modelling and analysis methods for economic characteristics variation in the innovation process have become a common technique, via employing diffusion equations for a medium with given parameters. The analysis results in this case significantly depend on the measurement accuracy of the industrial environment parameters, which is hard to achieve in practice. It seems, therefore, reasonable to make a transition from the diffusion paradigm to the innovation implementation paradigm, i.e., sequential modelling of the innovation states with variables and characteristics that correspond to the practical measurement and control techniques. Applying the described approach, the economic state dynamics of the innovation development work, manufacturing and implementation can be described by systems of ordinary differential equations, where the initial conditions and coefficients depend on the parameters of the industry’s internal andexternal environments. Two discrete mathematical models developed in this work enable control of the industrial environment parameters, via application of practical measurement methods. The first discrete model is in the form of a functional (mapping), which enables conversion of the actual internal industrial environment parameters in the beginning of the innovation scaling into the coefficients of the differential equations and initial conditions that reflect the results of manufacturing process preparation. The initial data is available from the EPR data base of the industry. The second discrete model is realized as a cellular automaton. An autonomous model of the external industrial environment uses the data that can be measured by the well-developed marketing methods. The results of the computational experiments support the hypothesis of transition from the diffusion model paradigm to the paradigm of the sequential modelling of the innovation economic states.
Белов В.Ф., Гаврюшин С.С., Маркова Ю.Н., Занкин А.И. Моделирование среды предприятия с использованием дискретных вычислительных алгоритмов.Математическое моделирование и численные методы, 2022, № 1, с. 109–128
004.942 Non-autonomous system as a model of the production process of technical innovation
Belov V. F. (МГУ им. Н.П. Огарева/АУ «Технопарк–Мордовия»), Gavryushin S. S. (Bauman Moscow State Technical University), Markova Y. N. (АУ «Технопарк–Мордовия»)
doi: 10.18698/2309-3684-2021-1-110131
Development of an analytical method for economic characteristics variation in an innovation process is a challenging task, starting from invention or entrepreneur idea and finishing with market implementation. The practical purpose here is both to minimize the risks and shorten design and implementation time. Theoretical and experimental results presented in the paper show the possibility to solve the mentioned task via applying a non-autonomous differential equation system along with the Lyapunov's First Method for stability analysis. A mathematical model of flow of funds related to the manufacturing and market implementation of the engineering innovation has been studied. It is presented in the form of the system of differential balance equations with unit impulse function in the right-hand side. An algorithm has been developed to analyze the stability of the equilibrium states of the manufacturing process, considering the influence of the external environment “from the right” (preparation of manufacturing) as well as “from the left” (market state). The requirements are determined regarding both the discrete model of manufacturing preparation to formulate initial conditions for the non-autonomous system and the discrete model of the market to calculate the market state dependent coefficients for the system of differential equations. The results of analysis of the economic characteristics variation in the stage of product manufacturing have been presented as 3D phase images.
Белов В.Ф., Гаврюшин С.С., Маркова Ю.Н. Неавтономная система как модель процесса производства технической инновации. Математическое моделирование и численные методы, 2021, № 1, с. 110–131.