Enhancing Team Productivity with Synergistic Work Patterns using Genetic Algorithms

Traditional productivity management systems usually fail to adapt to changing team dynamics and individual talents in today's dynamic business environment, resulting in inefficiencies and lower production. The article proposes a novel approach that uses Genetic Algorithms (GA) to improve work allocation and team composition. The proposed strategy uses GAs to detect cooperative work patterns, which improves team production. To achieve productivity objectives, the system adjusts team compositions and task assignments based on data, preprocessing, goal development, and GA implementation. When compared to current approaches, evaluation demonstrates significant improvements in project timeliness, team satisfaction, idle time reduction, and work completion rates. Specifically, the proposed solution reduces average idle time from 15 to 8 hours, increases team satisfaction from 6.2 to 8.5 (on a scale of 1 to 10), and reduces project completion time from 25 to 18 days. It also achieves a 91% work completion rate, vs 78% in the present system. Furthermore, the system demonstrates increased predictive performance and asset health monitoring. These findings demonstrate GAs' efficacy in improving team composition and efficiency, providing organizations with a competitive advantage in today's rapidly changing business climate.