Optimized Workload Assigning System Using Particle Swarm Optimization

Abstract

Workload is a key factor in faculty satisfaction and retention in Academic institutions. Excessive
workload was one of the top 2 reasons reported by faculty members for leaving an institution. Faculty
workload is an especially important consideration in colleges. A novel faculty workload model
proved to be effective in optimizing faculty workload within a academic institution. Since the
workload analysis, the faculty service commitment has been substantially changed, by reducing the
number of committees at our institution. When the number of records increased, it is difficult to
maintain the information of each staff in the old manual system. Maintaining the records manually
leads to error prone and required more man power and it consumes more time for processing the
records. This type of workload analysis may particularly benefit in any academic institution that
employ a team based learning curriculum, with a large time commitment to teaching. The model
breaks individual faculty member’s activities into well-defined units. An index for the individual and
the department is derived which represents productivity and workload. Costs of all faculty activities
including individual classes, advising, lab development, research, and others are derived. Student
credit hours produced, student faculty ratios, and other metrics are also computed in this system. The
whole process is done by the particle swarm optimization algorithm. This algorithm is used to have a
proper workload scheduling in order to assign a proper workload to the faculties. The Service
computing plays a functioning role in workload scheduling. Thus, these technologies makes this
system more efficient while progressing the system.