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Modeling of top management motivation in the electronics industry using a three-objective genetic algorithm

Vol. 30, Iss. 10, OCTOBER 2024

Received: 13 May 2024

Received in revised form: 27 May 2024

Accepted: 10 June 2024

Available online: 30 October 2024

Subject Heading: INVESTING

JEL Classification: C63, E17, O21, O36

Pages: 2184-2203

https://doi.org/10.24891/fc.30.10.2184

Sergei N. YASHIN National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
jashinsn@yandex.ru

https://orcid.org/0000-0002-7182-2808

Egor V. KOSHELEV National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
ekoshelev@yandex.ru

https://orcid.org/0000-0001-5290-7913

Aleksei A. IVANOV National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
alexey.iff@yandex.ru

https://orcid.org/0000-0003-4299-4042

Subject. This article discusses the issues of motivation of key executives in government institutions and manufacturing companies in the electronics industry.
Objectives. The article aims to simulate the motivation of key executives in government institutions and manufacturing companies in the electronics industry, using a three-objective genetic algorithm.
Results. The article presents the author-developed model of motivation of key executives in government institutions and manufacturing companies in the electronics industry using a three-objective genetic algorithm.
Conclusions and Relevance. The use of a three-objective genetic algorithm to model the motivation of key executives of the electronics industry helps obtain the necessary conclusions about the success of the development of this industry in the country's regions, since three target functions at once, depending on several input parameters, get maximized simultaneously. The results obtained can be useful to government institutions and manufacturing companies for planning the innovative development of the electronics industry.

Keywords: radio electronics industry, top management, motivation, multi-objective genetic algorithm

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