Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm
In recent years, there has been increasing interest in intelligent optimization algorithms, such as the Whale Optimization Algorithm (WOA). Initially proposed for continuous domains, WOA mimics the hunting behavior of humpback whales and has been adapted for discrete domains through modifications. T...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
The Science and Information (SAI) Organization Limited.
2025
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/47708/1/Optimizing%20Decentralized%20Exam.pdf http://ir.unimas.my/id/eprint/47708/ https://thesai.org/Publications/ViewPaper?Volume=16&Issue=1&Code=IJACSA&SerialNo=25 |
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| Summary: | In recent years, there has been increasing interest in intelligent optimization algorithms, such as the Whale Optimization Algorithm (WOA). Initially proposed for continuous domains, WOA mimics the hunting behavior of humpback whales and has been adapted for discrete domains through modifications. This paper presents a novel discrete Whale Optimization Algorithm approach, integrating the strengths of population-based and local-search algorithms for addressing the examination timetabling problem, a significant challenge many educational institutions face. This problem remains an active area of research and, to the authors’ knowledge, has not been adequately addressed by the WOA algorithm. The method was evaluated using real-world data from the first semester of 2023/2024 for faculties at the Universiti of Sarawak, Malaysia. The problem incorporates standard and faculty-specified constraints commonly encountered in real-world scenarios, accommodating online and physical assessments. These constraints include resource utilization, exam spread, splitting exams for shared and non-shared rooms, and period preferences, effectively addressing the diverse requirements of faculties. The proposed method begins by generating an initial solution using a constructive heuristic. Then, several search methods were employed for comparison during the improvement phase, including three Variable Neighborhood Descent (VND) variations and two modified WOA algorithms employing five distinct neighborhoods. These methods have been rigorously tested and compared against proprietary heuristic-based software and manual methods. Among all approaches, the WOA integrated with the iterative threshold-based VND approach outperforms the others. Furthermore, a comparative analysis of the current decentralized approach, decentralized with re-optimization, and centralized approaches underscores the advantages of centralized scheduling in enhancing performance and adaptability. |
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