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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: Emily Siew, Sing Kiang, Sze, San Nah, Goh, Say Leng
Format: Article
Language:English
Published: The Science and Information (SAI) Organization Limited. 2025
Subjects:
QA Mathematics
T Technology (General)
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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spelling my.unimas.ir-477082025-03-06T01:58:27Z http://ir.unimas.my/id/eprint/47708/ Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm Emily Siew, Sing Kiang Sze, San Nah Goh, Say Leng QA Mathematics T Technology (General) 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. The Science and Information (SAI) Organization Limited. 2025 Article PeerReviewed text en http://ir.unimas.my/id/eprint/47708/1/Optimizing%20Decentralized%20Exam.pdf Emily Siew, Sing Kiang and Sze, San Nah and Goh, Say Leng (2025) Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm. International Journal of Advanced Computer Science and Applications, 16 (1). pp. 257-265. ISSN 2156-5570 https://thesai.org/Publications/ViewPaper?Volume=16&Issue=1&Code=IJACSA&SerialNo=25 DOI: 10.14569/IJACSA.2025.0160125
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic QA Mathematics
T Technology (General)
spellingShingle QA Mathematics
T Technology (General)
Emily Siew, Sing Kiang
Sze, San Nah
Goh, Say Leng
Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm
description 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.
format Article
author Emily Siew, Sing Kiang
Sze, San Nah
Goh, Say Leng
author_facet Emily Siew, Sing Kiang
Sze, San Nah
Goh, Say Leng
author_sort Emily Siew, Sing Kiang
title Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm
title_short Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm
title_full Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm
title_fullStr Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm
title_full_unstemmed Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm
title_sort optimizing decentralized exam timetabling with a discrete whale optimization algorithm
publisher The Science and Information (SAI) Organization Limited.
publishDate 2025
url 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
_version_ 1825817121180876800
score 13.244413

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