A I Zankin (МГУ им. Н.П. Огарева) :


004.942 Mathematical Model of Distributed Design Toolkit Architecture

Belov V. F. (МГУ им. Н.П. Огарева/АУ «Технопарк–Мордовия»), Gavryushin S. S. (Bauman Moscow State Technical University), Zankin A. I. (МГУ им. Н.П. Огарева), Isaykin V. Y. (МГУ им. Н.П. Огарева)

doi: 10.18698/2309-3684-2024-1-110123

The aim of the article is to develop a method for distributing design tasks of mechanical engineering products among a given set of task performers. These task performers are structurally and geographically connected to their respective digital platforms, collectively forming a design ecosystem. A mathematical model has been developed, which can be successfully applied to generate the architecture of a toolkit covering requirements engineering, system architecture, and testing tasks for each project assigned to one of the platforms. The use of Petri nets is justified as a modeling method. Its implementation in the form of a software application for the Product Lifecycle Management (PLM) system of the digital platform can significantly improve project and portfolio management quality.

Белов В.Ф., Гаврюшин С.С., Занкин А.И., Исайкин В.Ю. Математическая модель архитектуры комплекса средств распределенного проектирования. Математическое моделирование и численные методы, 2024, № 1, с. 110–123.

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