The scheduling of academic departments is a challenging issue as it involves assigning courses to faculty based on their availability and qualifications while avoiding double allocation of classrooms and lectures.
In the paper, we tackle the problem of allocating faculty to courses for an academic year across multiple terms. The aim is to assign instructors to courses in an efficient and effective manner, considering all constraints, and giving priority to highly qualified and interested instructors. We use a Depth First Search algorithm that considers factors such as faculty availability, subject matter expertise, and class modality.
Optimal staffing problem is not only prevalent in academia but is also faced in various other industries where limited resources must be matched to available time slots. Automating the process of scheduling nurses in a hospital, for instance, can improve resource utilization significantly. Thus, the course staffing optimization solution presented in the paper can also be applied to other industries in critical situations such as the recent Covid-19 pandemic, allowing for effective and efficient utilization of resources like doctors, nurses, and lab technicians.
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