A METHOD FOR OPTIMIZING PROJECT PLANNING AND TEAM FORMATION USING A GENETIC ALGORITHM

Abstract

This work discusses the methodological and practical aspects of optimizing project planning and team formation using genetic algorithms. Particular emphasis is placed on the importance of proper technical equipment of the team, rationality of project planning and budgeting.

The main goal is to improve the efficiency of project management, reduce the risks of unsuccessful software projects, and increase the effectiveness of the resources used. Existing statistics show that only a small part of projects are successfully completed, so there is a need to apply the latest methods to improve their efficiency.

In addition to exploring the possibilities of using genetic algorithms in project planning, the article also considers the methodology of their application for personnel planning. In particular, attention is focused on the accurate modeling of the mathematical features of project planning and team formation. This allows us to choose the best strategies for genetic operations such as selection, crossover, and mutation aimed at maximizing fitness over generations. These techniques allows dynamically respond to changes in project conditions, optimizing resources and task execution time. Thanks to genetic algorithms, teams can reduce project implementation time while improving the quality of tasks. Key applications include assigning tasks to employees based on their qualifications and experience. Using hybrid coding, we managed to ensure effective task planning and distribution of responsibilities among the team. Based on the tests, it was found that stochastic selection of the initial population and genetic operations play a key role in the convergence of the algorithm. In general, the studies point to numerous advantages of using genetic algorithms in the field of project planning and team building, and question the need for further research in this area.