A Case Study of Model-Driven Engineering for Automated Timetabling

Eke, Clement; García-Domínguez, Antonio; Mourik, Jort van; Khan, Ifti

doi:10.1109/models-c.2019.00034

Cited by 2 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…University timetabling software has undergone significant evolution over the years by incorporating advanced optimization algorithms to schedule courses and exams. These algorithms consider various constraints, such as course conflicts, faculty and room availability, and student preferences, resulting in more efficient and effective scheduling solutions [1], [2]. Scheduling problems are common in various fields such as cloud computing [9], [10], manufacturing [11], [12], [13], [14], [15], computer science [16], and crew scheduling [17], [18].…”

Section: Related Workmentioning

confidence: 99%

“…The total number of groups taught by a faculty member m is given by equation (2). It is computed by summing over all courses, groups, and time slots for which the faculty member m is assigned, and their respective decision variables x klmi and v kln .…”

Section: Table I Displays the Parameters And Variables Used In The Ma...mentioning

confidence: 99%

“…As the size of universities and the number of courses and students continue to grow, the manual process of creating course timetables becomes more difficult and time-consuming. To address this issue, software solutions have been developed to automate the process of creating university timetables [1], [2]. The solutions presented are tailored for learning processes that do not account for the geographical proximity of faculty members to their assigned teaching campuses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Heuristics for Timetabling Scheduling in Blended Learning

Jarraya

2024

IJCDS

View full text Add to dashboard Cite

Blended learning, an innovative educational approach adopted by the Saudi Electronic University, combines traditional face-to-face teaching with online methods to improve educational outcomes. However, implementing it effectively faces challenges, especially in shifting physical classes to virtual formats for faculty and students spread across distant campuses, potentially compromising the core principles of blended learning and reducing its effectiveness. Ideally, each student would attend one face-to-face and one virtual session per course weekly. Yet, operational challenges emerge due to geographical disparities between faculty and student locations, necessitating the conversion of face-to-face sessions into virtual ones. To address these challenges, this study proposes two novel heuristic strategies for scheduling timetables in blended learning contexts. The first heuristic, called Minimum Load Accumulation Heuristic (MLAH), aims to evenly distribute teaching loads among faculty members and time slots while maximizing the number of groups assigned to faculty members from the same campus. The second heuristic, called Average Load Accumulation Heuristic (ALAH), calculates the average load of all faculty members and time slots and reduces the number of iterations searching for the minimum load, as performed by MLAH. These strategies aim to minimize the conversion of face-to-face sessions to virtual, ensure fair distribution of teaching responsibilities, and maintain a balanced allocation of face-to-face and virtual classes throughout the academic year. The paper demonstrates the effectiveness of these algorithms in producing high-quality solutions comparable to those generated by CPLEX, with significantly reduced computational complexity.

show abstract