Issues and challenges in class timetabling
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Class timetabling poses significant challenges due to its NP-hard nature, involving complex constraints and large search spaces. Various optimization techniques, such as genetic algorithms (GAs) and meta-heuristic methodologies, have been proposed to address these challenges. GAs, tailored with unique repair and improvement functions, have shown promise in efficiently assigning students to classes based on preferences . The timetabling problem is further complicated by faculty-related constraints, which require balancing faculty preferences and teaching loads while maximizing event assignments and minimizing unassigned events . Despite the availability of effective methodologies like meta-heuristics and hyper-heuristics, there is a gap in translating state-of-the-art solutions to real-world applications, possibly due to limited flexibility in these approaches . Overall, the field of class timetabling continues to evolve, with a focus on enhancing optimization algorithms and addressing the diverse constraints involved .
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| Papers (5) | Insight |
|---|---|
Open access•Dissertation 01 Jan 2019 | Challenges in class timetabling include limited classrooms, student-lecturer meeting times, and subject scheduling. Genetic algorithm optimization can efficiently address these issues in academic institutions like UTHM. |
31 Citations | Challenges in university course timetabling include complex constraints, large problem size, and underutilization of advanced methodologies despite the effectiveness of meta-heuristic and hybrid approaches. |
3 Citations | Challenges in class timetabling include faculty-course assignment constraints, manual errors, time consumption, and balancing university requirements while satisfying faculty preferences and student learning days. |
01 Jan 2023 | Challenges in class timetabling include NP complexity, combinatorial optimization, constraints, and the need for efficient algorithms like meta-heuristics. Multi-objective optimization is emerging for robust solutions. |
| Challenges in class timetabling include complexity, resource-intensive nature, and accommodating student preferences. A genetic algorithm proposed in the paper addresses these challenges effectively. |
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