Fuzzy Logic -- Based Scheduling System for Part-Time Workforce
This addresses scheduling inefficiencies for university administrators managing part-time student workforces, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of scheduling part-time student workers by applying a genetic fuzzy system to generate feasible schedules based on employee availability and preferences, demonstrating efficiency in meeting operational criteria and robustness in understaffed conditions.
This paper explores the application of genetic fuzzy systems to efficiently generate schedules for a team of part-time student workers at a university. Given the preferred number of working hours and availability of employees, our model generates feasible solutions considering various factors, such as maximum weekly hours, required number of workers on duty, and the preferred number of working hours. The algorithm is trained and tested with availability data collected from students at the University of Cincinnati. The results demonstrate the algorithm's efficiency in producing schedules that meet operational criteria and its robustness in understaffed conditions.