Optimization techniques in university timetabling problem: Constraints, methodologies, benchmarks, and open issues
University timetabling problems are a yearly challenging task and are faced repeatedly each semester. The problems are considered nonpolynomial time (NP) and combinatorial optimization problems (COP), which means that they can be solved through optimization algorithms to produce the aspired optimal...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tech Science Press
2023
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| Subjects: | |
| Online Access: | http://eprints.utm.my/106432/1/FuadAbdulgaleel2023_OptimizationTechniquesinUniversityTimetablingProblem.pdf http://eprints.utm.my/106432/ http://dx.doi.org/10.32604/cmc.2023.034051 |
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| Summary: | University timetabling problems are a yearly challenging task and are faced repeatedly each semester. The problems are considered nonpolynomial 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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