From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems
Solving the university timetabling problem is crucial as it ensures efficient use of resources, minimises scheduling conflicts, and enhances overall productivity. This paper presents a comprehensive review of university timetabling problems using integer programming algorithms. This study explores v...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/1/10 |
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| author | Xin Gu Muralee Krish Shaleeza Sohail Sweta Thakur Fariza Sabrina Zongwen Fan |
| author_facet | Xin Gu Muralee Krish Shaleeza Sohail Sweta Thakur Fariza Sabrina Zongwen Fan |
| author_sort | Xin Gu |
| collection | DOAJ |
| description | Solving the university timetabling problem is crucial as it ensures efficient use of resources, minimises scheduling conflicts, and enhances overall productivity. This paper presents a comprehensive review of university timetabling problems using integer programming algorithms. This study explores various integer programming techniques and their effectiveness in optimising complex scheduling requirements in higher education institutions. We analysed 95 integer programming-based models developed for solving university timetabling problems, covering relevant research from 1990 to 2023. The goal is to provide insights into the evolution of these algorithms and their impact on improving university scheduling. We identify that the implementation rate of models using integer programming is 98%, which is much higher than 34% implementation rates using meta-heuristics algorithms from the existing review. The integer programming models are analysed by the problem types, solutions, tools, and datasets. For three types of timetabling problems including course timetabling, class timetabling, and exam timetabling, we dive deeper into the commercial solvers CPLEX (47), Gurobi (11), Lingo (5), Open Solver (4), C++ GLPK (4), AIMMS (2), GAMS (2), XPRESS (2), CELCAT (1), AMPL (1), and Google OR-Tools CP-SAT (1) and identify that CPLEX is the most frequently used integer programming solver. We explored the uses of machine learning algorithms and the hybrid solutions of combining the integer programming and machine learning algorithms in higher education timetabling solutions. We also identify areas for future work, which includes an emphasis on using integer programming algorithms in other industrial areas, and using machine learning models for university timetabling to allow data-driven solutions. |
| format | Article |
| id | doaj-art-6ee6c1f9d0cf4758807ea11e5f6f19a0 |
| institution | Kabale University |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-01-01 |
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| series | Computation |
| spelling | doaj-art-6ee6c1f9d0cf4758807ea11e5f6f19a02025-01-24T13:27:47ZengMDPI AGComputation2079-31972025-01-011311010.3390/computation13010010From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling ProblemsXin Gu0Muralee Krish1Shaleeza Sohail2Sweta Thakur3Fariza Sabrina4Zongwen Fan5Lincoln Institute of Higher Education, Sydney, NSW 2000, AustraliaSchool of Information Technology, King’s Own Institute, Sydney, NSW 2000, AustraliaCollege of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW 2308, AustraliaSchool of Information Technology, King’s Own Institute, Sydney, NSW 2000, AustraliaSchool of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, AustraliaCollege of Computer Science and Technology, Huaqiao University, Xiamen 361021, ChinaSolving the university timetabling problem is crucial as it ensures efficient use of resources, minimises scheduling conflicts, and enhances overall productivity. This paper presents a comprehensive review of university timetabling problems using integer programming algorithms. This study explores various integer programming techniques and their effectiveness in optimising complex scheduling requirements in higher education institutions. We analysed 95 integer programming-based models developed for solving university timetabling problems, covering relevant research from 1990 to 2023. The goal is to provide insights into the evolution of these algorithms and their impact on improving university scheduling. We identify that the implementation rate of models using integer programming is 98%, which is much higher than 34% implementation rates using meta-heuristics algorithms from the existing review. The integer programming models are analysed by the problem types, solutions, tools, and datasets. For three types of timetabling problems including course timetabling, class timetabling, and exam timetabling, we dive deeper into the commercial solvers CPLEX (47), Gurobi (11), Lingo (5), Open Solver (4), C++ GLPK (4), AIMMS (2), GAMS (2), XPRESS (2), CELCAT (1), AMPL (1), and Google OR-Tools CP-SAT (1) and identify that CPLEX is the most frequently used integer programming solver. We explored the uses of machine learning algorithms and the hybrid solutions of combining the integer programming and machine learning algorithms in higher education timetabling solutions. We also identify areas for future work, which includes an emphasis on using integer programming algorithms in other industrial areas, and using machine learning models for university timetabling to allow data-driven solutions.https://www.mdpi.com/2079-3197/13/1/10integer programminguniversity timetablingsoft constraints and hard constraintsCPLEX |
| spellingShingle | Xin Gu Muralee Krish Shaleeza Sohail Sweta Thakur Fariza Sabrina Zongwen Fan From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems Computation integer programming university timetabling soft constraints and hard constraints CPLEX |
| title | From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems |
| title_full | From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems |
| title_fullStr | From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems |
| title_full_unstemmed | From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems |
| title_short | From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems |
| title_sort | from integer programming to machine learning a technical review on solving university timetabling problems |
| topic | integer programming university timetabling soft constraints and hard constraints CPLEX |
| url | https://www.mdpi.com/2079-3197/13/1/10 |
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