Optimization of Automatic Scheduling Algorithms for Biotechnology Course Allocation in Higher Education Institutes

Yunxia Jiang
Faculty of Education, Guangdong Baiyun University，Guangzhou 510900, Guangdong, China, St. Paul University Philippines, Tuguegarao City3500, Cagayan, Philippines.

DOI:https://doi.org/10.5912/jcb1193

Abstract:

The process of class planning and timetabling in higher education, particularly for biotechnology courses, poses a
complex and time-intensive challenge. Addressing this, the state-of-the-art automated class scheduling engine
Animal has been developed to streamline the scheduling process for biotechnology classes, programs, and
departments with a few clicks. This system proficiently avoids scheduling conflicts by integrating course details,
academic year schedules, and specific timetabling requirements, delivering optimized schedules swiftly. The
intelligent scheduling algorithm in Animal is tailored to accommodate staff and student preferences in the
biotechnology field, considering various factors such as holidays, room allocations, faculty availability, and hard
and soft constraints. Unlike traditional methods that require manual coordination of each task, this automated
system allows for iterations with multiple versions, enabling adjustments of preferences until a finely tailored
schedule for biotechnology courses is achieved. Manual interventions are also possible for further customization.
The primary objective of this study is to evaluate the effectiveness of the Automatic Scheduling Algorithm in the
specific context of biotechnology courses in higher education. The research presents a generic solution to the
complex scheduling challenges in this field. Unlike previous heuristics focusing predominantly on student
perspectives, our approach includes a comprehensive analysis of faculty preferences. Simulation studies explore
the time-delaying issues, and our proposed model employs a naïve Bayesian Algorithm to learn and adapt to
instructors' preferred days and times, thus addressing soft constraints effectively. Moreover, this study introduces
a novel application of cross-functional methodologies to develop an automated course scheduling system in
higher education, specifically for biotechnology courses, utilizing a hybrid Genetic-Ant algorithm. This innovative
approach streamlines the scheduling process and aligns it closely with the unique requirements of biotechnology
education, demonstrating a significant step forward in academic scheduling optimization.