Open Access

ARTICLE

Optimization Techniques in University Timetabling Problem: Constraints, Methodologies, Benchmarks, and Open Issues

by Abeer Bashab¹, Ashraf Osman Ibrahim^2,*, Ibrahim Abakar Tarigo Hashem³, Karan Aggarwal⁴, Fadhil Mukhlif⁵, Fuad A. Ghaleb⁵, Abdelzahir Abdelmaboud⁶

1 Faculty of Computer Science & Information Technology, Alzaiem Alazhari University, 13311, Khartoum, Sudan
2 Faculty Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, 88400, Sabah, Malaysia
3 Department of Computer Science, University of Sharjah, Sharjah, 27272, United Arab Emirates
4 Electronic and Communication Engineering Department, Maharishi Markandeshwar Deemed to be University, Mullana, Ambala, 133207, Haryana, India
5 Information Assurance and Security Research Group (IASRG), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia
6 Department of Information Systems, King Khaled University, Muhayel, 61913, Aseer, Saudi Arabia

* Corresponding Author: Ashraf Osman Ibrahim. Email:

Computers, Materials & Continua 2023, 74(3), 6461-6484. https://doi.org/10.32604/cmc.2023.034051

Received 05 July 2022; Accepted 10 October 2022; Issue published 28 December 2022

Abstract

University timetabling problems are a yearly challenging task and are faced repeatedly each semester. The problems are considered non-polynomial time (NP) and combinatorial optimization problems (COP), which means that they can be solved through optimization algorithms to produce the aspired optimal timetable. Several techniques have been used to solve university timetabling problems, and most of them use optimization techniques. This paper provides a comprehensive review of the most recent studies dealing with concepts, methodologies, optimization, benchmarks, and open issues of university timetabling problems. The comprehensive review starts by presenting the essence of university timetabling as NP-COP, defining and clarifying the two formed classes of university timetabling: University Course Timetabling and University Examination Timetabling, illustrating the adopted algorithms for solving such a problem, elaborating the university timetabling constraints to be considered achieving the optimal timetable, and explaining how to analyze and measure the performance of the optimization algorithms by demonstrating the commonly used benchmark datasets for the evaluation. It is noted that meta-heuristic methodologies are widely used in the literature. Additionally, recently, multi-objective optimization has been increasingly used in solving such a problem that can identify robust university timetabling solutions. Finally, trends and future directions in university timetabling problems are provided. This paper provides good information for students, researchers, and specialists interested in this area of research. The challenges and possibilities for future research prospects are also explored.

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Keywords

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