Modeling Employee Flexible Work Scheduling As A Classification Problem

Many organizations have adapted flexible working arrangements during COVID19 pandemic because of restrictions on the number of employees required on site at any time. Unfortunately, current employee scheduling methods are more suited for compressed working arrangements. The problem of automating compressed employee scheduling has been studied by many researchers and is widely adopted by many organizations in an attempt to achieve high quality scheduling. During process of employee scheduling many constraints may have to be considered and may require negotiating a large dimension of constraints like in flexible working. These constraints make scheduling a challenging task in these working arrangements. Most scheduling algorithms are modeled as constraint optimization problems and suited for compressed work but for flexible working with large constraint dimensions, achieving accurate scheduling is even more challenging. In this research, we propose a machine learning approach that takes advantage of mining user-defined constraints or soft constraints and transform employee scheduling into a classification problem. We propose automatically extracting employee personal schedules like calendars in order to extract their availability. We then show how to use the extracted knowledge in a multi-label classification approach in order to generate a schedule for faculty staff in a University that supports flexible working. We show that the results of this approach are comparable to that of a constraint satisfaction and optimization method that is commonly used in literature. Results show that our approach achieved accuracy of 93.1% of satisfying constraints as compared to 92.7% of a common constraint programming approach.