Developing Automated Workforce Scheduling System Using Machine Learning

Workforce scheduling in manufacturing environments is a critical factor that directly influences productivity, operational efficiency, and cost management. Traditional scheduling methods, such as manual processes and linear programming, often struggle to meet the needs of dynamic industries where sudden changes in demand and employee availability are common. This paper introduces an Automated Workforce Scheduling System (AWSS) using a Python-based program that employs multivariable regression algorithms to optimize workforce allocation. The AWSS leverages worker data, including skillsets, shift preferences, and operational demands, to generate schedules that meet constraints such as required skill coverage, cost minimization, and real-time adaptability to changes like absenteeism or spikes in workload. The system also provides a flexible solution to address inefficiencies present in traditional scheduling methods.