Keywords
lecture timetabling
Mann-Whitney test
scalable solutions
traditional scheduling methods
How to Cite
Abstract
This study tackles the complex challenge of lecture timetabling by incorporating lecturer day-off preferences, a crucial constraint often neglected in traditional scheduling methods. Given the NP-hard nature of the problem and the need for scalable solutions, a Genetic Algorithm (GA) was employed with a population size of 10, a crossover probability of 0.70, a mutation probability of 0.20, and a maximum generation of 10000. The proposed GA-based method, implemented using PHP and MySQL, is applied to a real-world scenario involving 25 courses, 22 lecturers, and six classrooms over a 5-day weekly schedule at the Faculty of Education and Teacher Training for the Even Semester of the 2023/2024 Academic Year. Experimental results, validated through the Mann-Whitney test, show that incorporating lecturer preferences enhances scheduling flexibility without significantly increasing computational time. Comparative analysis with Simulated Annealing and Tabu Search demonstrates the competitive performance of the GA-based method in optimizing lecture schedules. This study provides a practical solution for educational institutions seeking to improve their timetabling processes.
References
K. Khairunnisa, “Penjadwalan Perkuliahan Otomatis,” FIBONACCI J. Pendidik. Mat. Mat., vol. 1, no. 1, pp. 1–14, 2015.
E. Sugiarto, S. Winarno, and A. Fahmi, “Penjadwalan Perkuliahan Otomatis Berbasis Fuzzy Logic dan Genetic Algorithm pada Universitas Dian Nuswantoro,” Techno.COM J. Teknol. Inf., vol. 14, no. 4, pp. 315–328, 2015, doi: https://doi.org/10.33633/tc.v14i4.976.
N. R. Sabar, M. Ayob, G. Kendall, and R. Qu, “A Honey-bee Mating Optimization Algorithm for Educational Timetabling Problems,” Eur. J. Oper. Res., vol. 216, no. 3, pp. 533–543, 2012, doi: https://doi.org/10.1016/j.ejor.2011.08.006.
O. T. Arogundade, A. T. Akinwale, and O. M. Aweda, “A Genetic Algorithm Approach for a Real-World University Examination Timetabling Problem,” Int. J. Comput. Appl., vol. 12, no. 5, pp. 975 – 8887, Dec. 2010, doi: 10.5120/1678-2083.
S. N. Jat and S. Yang, “A Memetic Algorithm for the University Course Timetabling Problem,” in 2008 20th IEEE International Conference on Tools with Artificial Intelligence, 2008, vol. 1, pp. 427–433, doi: 10.1109/ICTAI.2008.126.
S. A. Mirhassani, “A computational approach to enhancing course timetabling with integer programming,” Appl. Math. Comput., vol. 175, no. 1, pp. 814–822, 2006, doi: 10.1016/j.amc.2005.07.039.
A. Darmawan and R. M. Hasibuan, “Penjadwalan Mata Kuliah Menggunakan Algoritma Genetika dengan Mempertimbangkan Team-Teaching,” in Simposium Nasional RAPI XIII, 2014, pp. 125–132.
M. U. Devi, J. Jayapradha, B. Naharas, and S. Sharma, “Automated timetable generation for academic institutions,” AIP Conf. Proc., vol. 3075, no. 1, p. 20094, Jul. 2024, doi: 10.1063/5.0217280.
S. Petrovic and E. Burke, “University Timetabling,” in Handbook of Scheduling: Algorithms, Models, and Performance Analysis, 1st ed., J. Y.-T. Leung, Ed. London: Chapman & Hall, 2004, pp. 45-1-45–19.
T. Birbas, S. Daskalaki, and E. Housos, “School Timetabling for Quality Student and Teacher Schedules,” J. Sched., vol. 12, no. 2, p. 177, 2008, doi: 10.1007/s10951-008-0088-2.
H. Kanoh and Y. Sakamoto, “Knowledge-based Genetic Algorithm for University Course Timetabling Problems,” KES J., vol. 12, pp. 283–294, Nov. 2008, doi: 10.3233/KES-2008-12403.
H. Raoofpanah and V. Ghezavati, “Extended hybrid tabu search and simulated annealing algorithm for location-inventory model with multiple products, multiple distribution centers and multiple capacity levels,” Prod. Eng., vol. 13, no. 6, pp. 649–663, 2019, doi: 10.1007/s11740-019-00919-x.
S. Bashath and A. R. Ismail, “Improved Particle Swarm Optimization by Fast Simulated annealing algorithm,” in 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), 2019, pp. 297–301, doi: 10.1109/ICAIIT.2019.8834515.
A. O. Bajeh and K. O. Abolarinwa, “Optimization: A Comparative Study of Genetic and Tabu Search Algorithms,” Int. J. Comput. Appl., vol. 31, no. 5, pp. 975–8887, 2011.
R. Gupta, P. Chadda, Y. K. Bhatt, A. Rawat, and G. Singh, “Genetic Algorithm-Based Timetable Creation System,” in 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), 2024, vol. 1, pp. 1–6, doi: 10.1109/ICEECT61758.2024.10739290.
M. Dorigo and C. Blum, “Ant colony optimization theory: A survey,” Theor. Comput. Sci., vol. 344, no. 2–3, pp. 243–278, 2005, doi: 10.1016/j.tcs.2005.05.020.
H. Alghamdi, T. Alsubait, H. Alhakami, and A. Baz, “A Review of Optimization Algorithms for University Timetable Scheduling,” Eng. Technol. Appl. Sci. Res., vol. 10, no. 6, pp. 6410–6417, 2020, doi: 10.48084/etasr.3832.
A. I. Diveev and O. V. Bobr, “Variational Genetic Algorithm for NP-hard Scheduling Problem Solution,” Procedia Comput. Sci., vol. 103, no. October 2016, pp. 52–58, 2017, doi: 10.1016/j.procs.2017.01.010.
E. A. Abdelhalim and G. A. El Khayat, “A Utilization-based Genetic Algorithm for Solving the University Timetabling Problem (UGA),” Alexandria Eng. J., vol. 55, no. 2, pp. 1395–1409, 2016, doi: 10.1016/j.aej.2016.02.017.
A. I. Diveev, O. V. Bobr, D. E. Kazaryan, and O. Hussein, “Some methods of solving the NP-difficult problem of optimal schedule for the university,” Procedia Comput. Sci., vol. 150, pp. 410–415, 2019, doi: 10.1016/j.procs.2019.02.071.
F. Gerges, G. Zouein, and D. Azar, “Genetic algorithms with local optima handling to solve sudoku puzzles,” in ACM International Conference Proceeding Series, 2018, pp. 19–22, doi: 10.1145/3194452.3194463.
W. Stannat, “On the convergence of genetic algorithms - A variational approach,” Probab. Theory Relat. Fields, vol. 129, no. 1, pp. 113–132, 2004, doi: 10.1007/s00440-003-0330-y.
S. Katoch, S. S. Chauhan, and V. Kumar, “A review on genetic algorithm: past, present, and future,” Multimed. Tools Appl., vol. 80, pp. 8091–8126, 2021, doi: 10.1007/s11042-020-10139-6.
A. Puspasari, K. Novianingsih, and F. Agustina, “Penyelesaian Masalah Penjadwalan Perkuliahan Menggunakan Algoritma Genetika (Studi Kasus Di Departemen Pendidikan Matematika Fpmipa Universitas Pendidikan Indonesia),” J. EurekaMatika, vol. 7, no. 1, pp. 80–92, 2019.
L. Paranduk, A. Indriani, M. Hafid, and Suprianto, “Sistem Informasi Penjadwalan Mata Kuliah Menggunakan Algoritma Genetika Berbasis Web,” in Seminar Nasional Aplikasi Teknologi Informasi (SNATi), 2018, pp. E46–E50.
P. Khairunisak and Y. Hendriyani, “Aplikasi Penjadwalan Perkuliahan Menggunakan Algoritma Genetika (Studi Kasus : Jurusan Teknik Elektronika FT - UNP),” Voteteknika J. Vocat. Tek. Elektron. dan Inform., vol. 9, no. 3, p. 24, 2021, doi: 10.24036/voteteknika.v9i3.112662.
A. Qashlim and M. Assiddiq, “Penerapan Algoritma Genetika untuk Sistem Penjadwalan Kuliah,” J. Ilm. Ilmu Komput., vol. 2, no. 1, pp. 1–6, 2016.
R. Hidayati, S. Bahri, and D. M. Midyanti, “Pengembangan Aplikasi Penjadwalan Matakuliah di Prodi Rekayasa Sistem Komputer Universitas Tanjungpura,” J. Tek. Inform. dan Sist. Inf., vol. 9, no. 3, pp. 2687–2698, 2022, doi: 10.35957/jatisi.v9i3.1499.
D. W. Nugraha, A. Y. E. Dodu, and A. T. S. Saud, “Sistem Penjadwalan Perkuliahan Menggunakan Algoritma Genetika (Studi Kasus Pada Jurusan Teknologi Informasi Fakultas Teknik Universitas Tadulako),” J. Ilm. Mat. dan Terap., vol. 14, no. 2, pp. 242–255, 2017, doi: 10.22487/2540766x.2017.v14.i2.9026.
Y. Afandi and W. Setiyaningsih, “Sistem Pejadwalan Kuliah Meggunakan Metode Algoritma Genetika pada Program Magister Fakultas Ekonomi dan Bisnis,” RAINSTEK J. Terap. Sains Teknol., vol. 1, no. 1, pp. 40–47, 2019, doi: 10.21067/jtst.v1i1.3069.
H. Ardiansyah and M. B. S. Junianto, “Penerapan Algoritma Genetika untuk Penjadwalan Mata Pelajaran,” J. Media Inform. Budidarma, vol. 6, no. 1, p. 329, 2022, doi: 10.30865/mib.v6i1.3418.
N. L. G. P. Suwirmayanti, I. M. Sudarsana, and S. Darmayasa, “Penerapan Algoritma Genetika Untuk Penjadwalan Mata Pelajaran,” J. Appl. Intell. Syst., vol. 1, no. 3, pp. 220–233, 2016.
F. Mone and J. E. Simarmata, “Aplikasi Algoritma Genetika dalam Penjadwalan Mata Kuliah,” BAREKENG J. Ilmu Mat. dan Terap., vol. 15, no. 4, pp. 615–628, 2021, doi: 10.30598/barekengvol15iss4pp615-628.
W. A. Puspaningrum, A. Djunaidy, and R. A. Vinarti, “Penjadwalan Mata Kuliah Menggunakan Algoritma Genetika,” J. Tek. POMITS, vol. 2, no. 1, pp. 153–160, 2013.
D. C. Hajariwala, S. S. Patil, and S. M. Patil, “A Review of Metaheuristic Algorithms for Job Shop Scheduling,” Eng. Access, vol. 11, no. 1, pp. 65–91, 2025, doi: 10.14456/mijet.2025.7.
O. Maghiar et al., “Behaviour Analysis of Trajectory and Population-Based Metaheuristics on Flexible Assembly Scheduling BT - Metaheuristics,” in Metaheuristics International Conference, 2024, pp. 80–95, doi: 10.1007/978-3-031-62922-8_6.
X. Weng, W. She, H. Fan, J. Zhang, and L. Yun, “Multi-depot vehicle routing problem with drones in emergency logistics,” Cluster Comput., vol. 28, no. 1, p. 64, 2024, doi: 10.1007/s10586-024-04809-5.
L. Xiong and F. Yuan, “The Impact of Teacher Work Engagement on Student Engagement: Teaching Quality as a Mediator,” Soc. Behav. Personal. An Int. J., vol. 52, no. 9, pp. 1–8, 2024, doi: 10.2224/sbp.13541.
A. Fikri Zaidan and L. Juariyah, “The Influence of Workloads on the Job Satisfaction of the Lecturers of State University of Malang Through Job Stress as Intervening Variable,” in International Conference on Islam, Economy, and Halal Industry, 2020, vol. 2020, pp. 156–176, doi: 10.18502/kss.v4i9.7323.
D. Lestari and Rahardianto, “Work Life Balance And Job Satisfaction Of Lecturer In Faculty Of Economics And Business Unjani,” Int. J. Sci. Technol. Manag., vol. 2, no. 5, pp. 1491–1504, 2021, doi: 10.46729/ijstm.v2i5.315.
L. Kim, P. Pongsakornrungsilp, S. Pongsakornrungsilp, N. Horam, and V. Kumar, “Key Determinants of Job Satisfaction among University Lecturers,” Soc. Sci., vol. 12, no. 3, p. 153, 2023, doi: 10.3390/socsci12030153.
E. Siahaan, P. Gultom, K. A. Fachrudin, and A. M. D. Sitohang, “Success Factors to Optimize the Job Satisfaction and Achieve Better Performance of Lecturers in Higher Education,” GATR Glob. J. Bus. Soc. Sci. Rev., vol. 10, no. 1, pp. 12–21, 2022, doi: 10.35609/gjbssr.2022.10.1(2).
P. Sedgwick, “Non-parametric statistical tests for two independent groups: Numerical data,” BMJ, vol. 348, no. February, pp. 9–11, 2014, doi: 10.1136/bmj.g2907.
R. Chen, L. Su, and J. Guan, “AI-Powered Genetic Algorithm for Optimal Scheduling in Medical Research and Educational Management,” Comput. Aided. Des. Appl., vol. 21, no. S24, pp. 17–34, 2024, doi: 10.14733/cadaps.2024.S24.17-34.
K. N. Subang, E. I. Balaba, and J. C. A. Jr, “Optimizing Course Scheduling with Genetic Algorithms : A Dynamic Approach,” SAR J., vol. 7, no. 4, pp. 296–302, 2024, doi: 10.18421/SAR74-02.
K. L. Mills, J. J. Filliben, and A. L. Haines, “Determining relative importance and effective settings for genetic algorithm control parameters,” Evol. Comput., vol. 23, no. 2, pp. 309–342, 2015, doi: 10.1162/EVCO_a_00137.
P. L. Iglesias, D. Mora, F. Javier Martinez, and V. S. Fuertes, “Study of sensitivity of the parameters of a genetic algorithm for design of water distribution networks,” J. Urban Environ. Eng., vol. 1, no. 2, pp. 61–69, 2007, doi: 10.4090/juee.2007.v1n2.061069.
A. H. Khan and T. Imtiaz, “A Novel Genetic Algorithm Based Timetable Generator for Optimized University Timetable Solution,” in 2024 International Conference on Engineering & Computing Technologies (ICECT), 2024, pp. 1–6, doi: 10.1109/ICECT61618.2024.10581296.
M. A. Lopes, “A Metaheuristic Approach to Improving University Timetables Using Genetic Algorithms and Simulated Annealing,” Universidade do Porto, 2024.
S. Saraeian, “Sustainable Distributed Scheduling Problem,” Vietnam J. Comput. Sci., pp. 1–26, 2024, doi: 10.1142/S2196888824500222.
C. C. Le, T. A. T. Nguyen, M. Q. Tran, and T. N. Phan, “Solving the Vietnamese EdTech Timetabling Problem with Optimized Multi-Objectives BT - Multi-disciplinary Trends in Artificial Intelligence,” 2025, pp. 334–346.
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