Development of an automated system for task distribution and employee workload monitoring

Abstract

Modern workplaces require efficient task management and workload distribution to maintain productivity and employee well-being. This research presents a novel approach to developing an automated task distribution and workload monitoring system, integrating machine learning algorithms and optimization techniques. The system is designed to assign tasks dynamically based on employee availability, skills, and real-time workload data. The core methodology combines task scheduling algorithms and neural network-based predictive models to forecast employee performance under varying workloads. Key contributions include developing an adaptive system capable of continuous workload assessment and integrating employee feedback to improve task assignment strategies. The results demonstrate significant improvements in task allocation efficiency and workload balance, contributing to overall team performance. The system's modular structure allows easy integration with existing project management tools. Future research will explore advanced machine learning models to enhance predictive accuracy and user satisfaction.