Intelligent Model for Ranking IT Specialists Based on Competencies, Team Roles, and Resource Workload Indicators
Тип публікації :
Тези конференції
Дата випуску :
11 травня 2026 р.
Автор(и) :
Korytnyi, Oleksii
Taras Shevchenko National University of Kyiv
Мова основного тексту :
Англійська
eKNUTSHIR URL :
Конференція/Видання :
4th International Scientific and Practical Conference «Modern Scientific Research: Theoretical and Practical Aspects», May 11-13, 2026, Riga, Latvia
Початкова сторінка :
246
Кінцева сторінка :
257
Цитування :
[APA 7] Ziuziun, V., & Korytnyi, O. (2026). Intelligent model for ranking IT specialists based on competencies, team roles, and resource workload indicators. In Modern Scientific Research: Theoretical and Practical Aspects: Proceedings of the 4th International Scientific and Practical Conference, May 11–13, 2026 (pp. 246–257). European Open Science Space. https://doi.org/10.70286/EOSS-11.05.2026.009.246-257
[ДСТУ] Ziuziun V., Korytnyi O. Intelligent Model for Ranking IT Specialists Based on Competencies, Team Roles, and Resource Workload Indicators. Modern Scientific Research: Theoretical and Practical Aspects : Proceedings of the 4th International Scientific and Practical Conference (Riga, Latvia, May 11–13, 2026). Riga : European Open Science Space, 2026. P. 246–257. DOI: 10.70286/EOSS-11.05.2026.009.246-257 (date of access: 01.08.2026).
The article presents the results of developing and formalizing a conceptual model of an intelligent information system designed to optimize human resource management processes in the IT industry. The relevance of the study stems from the need to overcome the fragmentation of data involved in forming project teams, where traditional methods often overlook the interdependence between technical competencies, psychological compatibility, and the actual workload of personnel. The author proposes a comprehensive approach to the automated ranking of specialists based on the integration of multidimensional input data, including professional competencies (Hard Skills), soft skills, team roles according to the Belbin methodology, and dynamic administrative indicators. Particular attention is devoted to the formalization of evaluation criteria, among which the level of autonomy, domain expertise, and specific parameters of resource availability are of critical importance. The scientific novelty of the work lies in the development of a mathematical framework for calculating an integral indicator of candidate effectiveness that accounts for nonlinear organizational constraints, including a maximum workload threshold of 85 % and the risks of professional burnout assessed through the analysis of vacation and sick leave usage dynamics. The model implements a «logical filter» mechanism based on project roles, enabling the system to instantly exclude irrelevant candidates and concentrate the analysis on a targeted group of specialists. The practical significance of the study is demonstrated through the description of operational scenarios for the system’s intelligent core, which is capable of automatically balancing team composition by prioritizing missing behavioral roles even in cases of minor deviations
in technical qualifications. The proposed model also incorporates technical and legal constraints, including compliance with data protection requirements under the General Data Protection Regulation (GDPR), making it a robust foundation for designing highly reliable web applications in the field of human resource management. The results of the study establish a foundation for transitioning from subjective personnel selection to intelligent talent management based on objective metrics and predictive analytics.
in technical qualifications. The proposed model also incorporates technical and legal constraints, including compliance with data protection requirements under the General Data Protection Regulation (GDPR), making it a robust foundation for designing highly reliable web applications in the field of human resource management. The results of the study establish a foundation for transitioning from subjective personnel selection to intelligent talent management based on objective metrics and predictive analytics.
Галузі знань та спеціальності :
F Інформаційні технології
Галузі науки і техніки (FOS) :
Інженерія та технології
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