AI-Optimized Workforce Scheduling for Enhanced Industrial Efficiency: A PSO-Based Approach

Using Particle Swarm Optimization (PSO), it delivers an AI-optimized workforce scheduling solution designed for industrial contexts. The research delves into the difficulties of maximizing labor distribution to improve output effectiveness while lowering expenses and idle time. PSO was used to create schedules that take into account various limitations, such as the availability of workers, job priority, and production objectives. To verify the efficacy of the suggested schedules, the production process was simulated in real time using MATLAB Simulink. Significant gains are shown by the data, which show that production downtime was minimized to 3.2%, labor utilization raised to 92%, and the average job completion time was lowered to 8.5 hours. Employee satisfaction increased to 87%, while scheduling efficiency increased to 95%. These results show how well-suited and reliable the PSO-based scheduling system is for handling the changing demands of production settings. The results indicate that this strategy is a useful tool for upcoming production issues as it not only maximizes operational efficiency but also boosts worker morale.