Mathematical model for university online exam scheduling considering lecturer preferences

Evi Febianti, Muhammad Adha Ilhami, Arif Hidayat

Abstract


Zoom accounts are used as an alternative for online teaching and learning activities during the pandemic. The Industrial Engineering Study Program at Sultan Ageng Tirtayasa has been scheduling online exams and determining the number of Zoom accounts needed manually, which has resulted in an insufficient number of Zoom accounts to meet all course schedules. Proper online exam scheduling can optimize the number of Zoom accounts required. This study aims to determine the number of Zoom accounts needed for online exams by developing a mathematical model. The model developed is an Integer Linear Programming (ILP) model, with the objective of minimizing the number of Zoom accounts required. The problem is solved using an optimization approach with deterministic parameters. The model provides recommendations for decision-making in the implementation of online exams, including determining the exam timetable and the number of Zoom accounts to be rented. The computational results show that the developed model provides an optimal solution, requiring only three Zoom accounts.

Keywords


Branch and bound; Integer linear programming; Optimization; Scheduling

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References


F. Irmada and I. Yatri, “Keefektifan Pembelajaran Online Melalui Zoom Meeting di Masa Pandemi bagi Mahasiswa,” J. Basicedu, vol. 5, no. 4, 2021.

J. Monica and D. Fitriawati, “Efektivitas Penggunaan Aplikasi Zoom Sebagai Media Pembelajaran Online Pada Mahasiswa Saat Pandemi Covid-19,” J. Communio J. Jur. Ilmu Komun., vol. 9, no. 2, 2020, doi: 10.35508/jikom.v9i2.2416.

G. I. E. Soen, M. Marlina, and R. Renny, “Implementasi Cloud Computing dengan Google Colaboratory pada Aplikasi Pengolah Data Zoom Participants,” JITU J. Inform. Technol. Commun., vol. 6, no. 1, 2022, doi: 10.36596/jitu.v6i1.781.

R. E. Putri H. and T. A. Wulandari, “Pemanfaatan aplikasi Zoom cloud meeting sebagai media e-learning dalam mencapai pemahaman mahasiswa di tengah pandemi COVID-19,” J. Common, vol. 4, no. 2, 2021, doi: 10.34010/common.v4i2.4436.

M. Lindahl, A. J. Mason, T. Stidsen, and M. Sørensen, “A strategic view of University timetabling,” Eur. J. Oper. Res., vol. 266, no. 1, 2018, doi: 10.1016/j.ejor.2017.09.022.

M. Mokhtari, M. Vaziri Sarashk, M. Asadpour, N. Saeidi, and O. Boyer, “Developing a Model for the University Course Timetabling Problem: A Case Study,” Complexity, vol. 2021, 2021, doi: 10.1155/2021/9940866.

M. C. Torres, K. K. S. Villegas, and M. K. A. Gavina, “Solving faculty-course allocation problem using integer programming model,” Philipp. J. Sci., vol. 150, no. 4, 2021.

N. A. H. Aizam, N. F. Jamaluddin, and S. Ahmad, “A survey on the timetabling communities’ demands for an effective examination timetabling in universiti malaysia terengganu,” Malaysian J. Math. Sci., vol. 10, 2016.

H. A. Taha, “Riset Operasi,” Jakarta: Bina Rupa Aksara, vol. 3, no. 2. 1996.

D. Wungguli and N. Nurwan, “Penerapan Model Integer Linear Programming Dalam Optomasi Penjadwalan Perkuliahan Secara Otomatis,” Barekeng J. Ilmu Mat. dan Terap., vol. 14, no. 3, 2020, doi: 10.30598/barekengvol14iss3pp413-424.

O. Czibula, H. Gu, A. Russell, and Y. Zinder, “A multi-stage IP-based heuristic for class timetabling and trainer rostering,” Ann. Oper. Res., vol. 252, no. 2, 2017, doi: 10.1007/s10479-015-2090-3.

Y. Chung and H. Kim, “The University Examination and Course Timetabling Problem With Integer Programming,” J. Korea Soc. Comput. Inf., vol. 24, no. 9, 2019.

M. Akif Bakir and C. Aksop, “A 0-1 integer programming approach to a university timetabling problem,” Hacettepe J. Math. Stat., vol. 37, no. 1, 2008.

D. Lalang and D. R. Alohaja, “Penggunaan integer linier programming untuk meminimumkan ruang kuliah pada Mata Kuliah Dasar Umum (MKDU) Studi Kasus di Universitas Tribuana Kalabahi,” J. Saintek Lahan Kering, vol. 4, no. 2, 2022, doi: 10.32938/slk.v4i2.1554.

A. E. Phillips, C. G. Walker, M. Ehrgott, and D. M. Ryan, “Integer programming for minimal perturbation problems in university course timetabling,” Ann. Oper. Res., vol. 252, no. 2, 2017, doi: 10.1007/s10479-015-2094-z.

H. Algethami and W. Laesanklang, “A mathematical model for course timetabling problem with faculty-course assignment constraints,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3103495.

M. A. Ilhami, Subagyo, and N. A. Masruroh, “A mathematical model at the detailed design phase in the 3DCE new product development,” Comput. Ind. Eng., vol. 146, 2020, doi: 10.1016/j.cie.2020.106617.

V. Pereira and H. Gomes Costa, “Linear integer model for the course timetabling problem of a faculty in rio de janeiro,” Adv. Oper. Res., vol. 2016, 2016, doi: 10.1155/2016/7597062.

Z. Mohd Zaulir, N. L. A. Aziz, and N. A. H. Aizam, “A General Mathematical Model for University Courses Timetabling: Implementation to a Public University in Malaysia,” Malaysian J. Fundam. Appl. Sci., vol. 18, no. 1, 2022, doi: 10.11113/MJFAS.V18N1.2408.

M. Khamechian and M. E. H. Petering, “A mathematical modeling approach to university course planning,” Comput. Ind. Eng., vol. 168, 2022, doi: 10.1016/j.cie.2021.107855.

E. Guzman, R. Poler, and B. Andres, “A matheuristic approach combining genetic algorithm and mixed integer linear programming model for production and distribution planning in the supply chain,” Adv. Prod. Eng. Manag., vol. 18, no. 1, 2023, doi: 10.14743/APEM2023.1.454.




DOI: http://dx.doi.org/10.62870/jiss.v10i2.28571

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