Course Schedule Optimization Using a Java-Based Ant Colony Optimization
DOI:
https://doi.org/10.35194/mji.v17i2.5961Keywords:
ACO Algorithm, course scheduling, Java programming, metaheuristic, optimizationAbstract
Course timetabling in higher education is a complex combinatorial problem due to constraints related to lecturer availability, limited classroom resources, and fixed weekly time-slot structures. As the number of courses and class sections increases, manual scheduling becomes increasingly inefficient and prone to conflicts, particularly room clashes and overlapping lecturer assignments. This study develops and evaluates an automatic course scheduling system based on the Ant Colony Optimization (ACO) algorithm and implements it as a Java-based desktop application to generate feasible timetables under real institutional conditions. An experimental computational approach is employed, in which artificial ants construct candidate schedules through probabilistic selection influenced by pheromone trails and heuristic information. Timetable quality is evaluated using a weighted cost function that prioritizes hard-constraint satisfaction, such as preventing lecturer and room clashes, while also incorporating soft-constraint penalties related to lecturer forbidden timeslots and schedule distribution balance. The system is tested using real academic data from an undergraduate study program, including courses, lecturers, classrooms, and predefined weekly timeslots. Experimental results show that the proposed system consistently generates conflict-free timetables, achieving a conflict value of zero across all repeated runs under the selected parameter configuration. Beyond feasibility, the optimization process continues to refine timetable quality by reducing soft-constraint penalties, as indicated by the convergence behavior observed across repeated executions. This repeated-run evaluation provides insight into the stochastic optimization characteristics of the ACO-based approach under fixed parameter settings. These findings indicate that the Java-based ACO approach effectively supports automated university course scheduling and provides a practical solution for producing feasible and well-structured timetables.References
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[21] H. M. H. Bestari, P. P. Suryadhini, and Nopendri, “Flow shop scheduling using a combination of ant colony optimization algorithm and tabu search algorithm to minimize total tardiness,” Jurnal Teknik Industri, vol. 10, no. 2, pp. 383–392, 2024.
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[2] M. S. Shiri, S. M. Khorramizadeh, and V. Ahmadi, “New heuristic local search method for university course timetabling problem,” Innovation Management and Operational Strategies, vol. 3, no. 4, pp. 452–464, 2022, doi: 10.22105/imos.2022.332030.1215.
[3] S. Ceschia, L. Di Gaspero, and A. Schaerf, “Educational timetabling: problems, benchmarks, and state-of-the-art,” European Journal of Operational Research, vol. 308, no. 1, pp. 1–18, 2023, doi: 10.1016/j.ejor.2022.07.011.
[4] R. Ge and J. Chen, “Analysis of college course scheduling problem based on ant colony algorithm,” Computational Intelligence and Neuroscience, vol. 2022, Article ID 7918323, 2022, doi: 10.1155/2022/7918323.
[5] C. B. C. Mallari, J. L. G. San Juan, and R. C. Li, “The university coursework timetabling problem: an optimization approach to synchronizing course calendars,” Computers & Industrial Engineering, vol. 184, Article 109561, 2023, doi: 10.1016/j.cie.2023.109561.
[6] S. Abdipoor, R. Yaakob, S. L. Goh, and S. Abdullah, “Meta-heuristic approaches for the university course timetabling problem,” Intelligent Systems with Applications, vol. 19, Article 200253, 2023, doi: 10.1016/j.iswa.2023.200253.
[7] S. Potluri and K. S. Rao, “Optimization model for QoS based task scheduling in cloud computing environment,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 18, no. 2, pp. 1081–1088, 2020, doi: 10.11591/ijeecs.v18.i2.pp1081-1088.
[8] Al-Mahmud, “Highly constrained university class scheduling using ant colony optimization,” International Journal of Computer Science and Information Technology, vol. 13, no. 1, pp. 21–32, 2021, doi: 10.5121/ijcsit.2021.13102.
[9] Y. Ikhwani, K. Marzuki, and A. Ramadhan, “Automated university lecture schedule generator based on evolutionary algorithm,” Matrik: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 22, no. 1, pp. 129–138, 2022, doi: 10.30812/matrik.v22i1.2215.
[10] Z. Gu, Z. Lü, and J.-K. Hao, “From integer programming to machine learning: a technical review on solving university timetabling problems,” Computation, vol. 12, no. 1, Article 12, 2024, doi: 10.3390/computation12010012.
[11] R. Hidayat, H. Herlina, M. Abduh, and I. Y. Wulandari, “Optimasi berbasis swarm intelligence untuk penjadwalan mata kuliah di perguruan tinggi,” Jurnal Teknologi dan Sains Modern, vol. 2, no. 3, pp. 114–123, 2025, doi: 10.69930/jtsm.v2i3.380.
[12] D. Romaguera, J. Plender-Nabasa, J. Matias, and L. Austero, “Development of a web-based course timetabling system based on an enhanced genetic algorithm,” Procedia Computer Science, vol. 234, pp. 1714–1721, 2024, doi: 10.1016/j.procs.2024.03.177.
[13] A. Johan, R. Pratama, and D. Siregar, “Optimization of university course scheduling using metaheuristic approaches,” Journal of Informatics and Computer Science, vol. 8, no. 1, pp. 45–54, 2024.
[14] M. Davison and A. Kheiri, “Modelling and solving the university course timetabling problem with hybrid teaching considerations,” Journal of Scheduling, vol. 28, no. 2, pp. 195–215, 2025, doi: 10.1007/s10951-024-00817-w.
[15] T. Purniawan, M. Fahmi, and Salmon, “Penerapan information architecture untuk optimalisasi website sistem informasi akademik (SIAK) STMIK Widya Cipta Dharma,” Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 8, no. 3, pp. 1174–1184, 2025.
[16] C. Blum, “Ant colony optimization: a bibliometric review,” Physics of Life Reviews, vol. 51, pp. 87–95, 2024, doi: 10.1016/j.plrev.2024.09.014.
[17] M. Chen, F. Werner, and M. Shokouhifar, “Mathematical modeling and exact optimizing of university course scheduling considering preferences of professors,” Axioms, vol. 12, no. 5, Article 498, 2023, doi: 10.3390/axioms12050498.
[18] H. J. Christanto and Y. A. Singgalen, “Analysis and design of student guidance information system through software development life cycle (SDLC) and waterfall model,” Journal of Information Systems and Informatics, vol. 5, no. 1, pp. 259–270, 2023, doi: 10.51519/journalisi.v5i1.443.
[19] M. Neroni, “Ant colony optimization with warm-up,” Algorithms, vol. 14, no. 10, Article 295, 2021, doi: 10.3390/a14100295.
[20] A. Perez-Napalit, “Ant-inspired scheduling: integrating constraint satisfaction problem with ant colony optimization,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 13, no. 3, pp. 112–118, 2024, doi: 10.30534/ijatcse/2024/031332024.
[21] H. M. H. Bestari, P. P. Suryadhini, and Nopendri, “Flow shop scheduling using a combination of ant colony optimization algorithm and tabu search algorithm to minimize total tardiness,” Jurnal Teknik Industri, vol. 10, no. 2, pp. 383–392, 2024.
[22] M. S. Shiri, S. M. Khorramizadeh, and V. Ahmadi, “New heuristic local search method for university course timetabling problem,” Innovation Management and Operational Strategies, vol. 3, no. 4, pp. 452–464, 2022, doi: 10.22105/imos.2022.332030.1215.
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2025-12-31
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