Online/offline Course and Multiple Lecturers Scheduling Using Meta-Heuristic Approaches
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Publication Details
Author list: Rodprasert, Nampetch; Taetragool, Unchalisa; Akkarajitsakul, Khajonpong
Publisher: Association for Computing Machinery
Publication year: 2023
Start page: 166
End page: 171
Number of pages: 6
ISBN: 978-145039957-9
Languages: English-Great Britain (EN-GB)
Abstract
The university course timetabling problem (UCTTP) is one of the university's real-world challenging problems. The practical timetable must be scheduled so that it is suitable for lecturers, students, and available resources. The complexity of the university course scheduling problem, thus, arises from the simultaneous existence of several constraints and the large number of possible solutions. Moreover, each university may have its own set of constraints or distinguishing characteristics. In this study, a mathematical model with two new constraints that reflect the current event of the real-world situation is proposed. The first constraint is related to the COVID-19 situation, which allows for the simultaneous scheduling of online and offline courses, and the second constraint is related to the scheduling of courses with multiple lecturers. Both constraints allow online/offline courses and courses with multiple lecturers to be scheduled at the same timeslot, unlike traditional constraints that only allow one course or one lecturer per timeslot. Even though the proposed constraints complicate the university course timetabling problem, they provide a better reflection on a real-world problem. Two meta-heuristic approaches will be used to solve the proposed model: the Genetic Algorithm (GA) and the Harmony Search Algorithm (HS). Integer programming is used as a benchmark algorithm. The experimental findings demonstrate that GA outperforms HS and integer programming in terms of cost, unscheduled course hours, and practicality. ฉ 2023 ACM.
Keywords
Harmony Search Algorithm, Meta-heuristic Algorithm, University Course Timetabling
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