Large-scale timetabling problems with adaptive tabu search

Awad, Fouad H.; Al-Kubaisi, Ali; Mahmood, Maha

doi:10.1515/jisys-2022-0003

Cited by 11 publications

(9 citation statements)

References 20 publications

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“…The IP/LP method is influenced by the size of the institute and obtaining optimal solutions can be difficult. [30] proposed a heuristic approach to solve this problem by separating the timetabling problem into two sub-problems: timetabling sub-problem and grouping sub-problem. [31], [32] presented the formulation of the UCTTP problem using integer programming, along with a case study.…”

Section: Related Workmentioning

confidence: 99%

Efficient Heuristics for Timetabling Scheduling in Blended Learning

Jarraya

2024

IJCDS

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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.

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Section: Related Workmentioning

confidence: 99%

Efficient Heuristics for Timetabling Scheduling in Blended Learning

Jarraya

2024

IJCDS

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“…The most popular metaheuristics for solving timetabling events are the Genetic Algorithm (Mittal, Doshi, Sunasra, & Nagpure, 2015) and, Tabu Search (Muklason, Irianti, & Marom, 2019;Arntzen & Løkketangen, 2005). Some researchers such as Awad, Al-kubaisi, and Mahmood (2022) and, Lu and Hao (2008) apply the Adaptive Tabu Search to generate feasible timetables. The teaching timetabling problem involves the distribution of resources to avoid clashes between or among them (Abdellahi & Eledum, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Erosion And Deposition Metaheuristic University Teaching Timetabling Framework

Murairwa¹

2023

IJRP

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The research developed a Hybrid Erosion And Deposition Metaheuristic University Teaching Timetabling Algorithm (HEADMUTTA). The hybrid erosion and deposition metaheuristic university teaching timetabling algorithm constructs a university teaching timetable with very few iterations. The Hybrid Erosion And Deposition (HEAD) metaheuristic university teaching timetabling algorithm adopts its behaviour from the HEAD metaheuristic with some concepts adapted from Tabu Search, Simulated Annealing, and Ant Colony metaheuristics. The HEADMUTTA constructs a draft university teaching timetable and further improves it by searching for the best feasible solution that satisfies the predetermined soft and hard rules or constraints. The research also proposed a Hybrid Erosion and Deposition Metaheuristic University Teaching Timetabling framework for implementation. The results indicate that the use of hybrid metaheuristics to solve university teaching timetabling problems improves the quality of the produced university teaching timetables. The HEAD metaheuristic algorithm has a unique feature that allows it to further improve the draft university teaching timetable. Further improvements in the framework may reduce the complexity of NP-hard combinatorial teaching timetabling problems.

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“…Metaheuristics are also employed in the scheduling of exams, such as the Tabu algorithm [29]. It is a very old approach, with poor performance in quite complex scenarios because of the cumbersome way in which problems are described.…”

Section: State Of the Artmentioning

confidence: 99%

Automated Activity Scheduling Tools for Improving Learning and Evaluation of Cybersecurity Competencies in Computer Engineering Courses

Bordel

Alcarria

Robles

2023

Int. J. Emerg. Technol. Learn.

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In the last ten years, many different innovative learning methodologies have been proposed for engineering education. In those new approaches, students learning, and evaluation are usually supported by a catalogue of creative activities, practices, and presentations which may be very numerous if competencies to be acquired are heterogeneous and must cover a large knowledge area, such as in cybersecurity courses. In addition, when a high number of students are enrolled in the course, deadlines must be strict, to enable professors to evaluate all students and activities properly and on time. Due to this requiring schedule, students often cannot work all competencies with the expected depth, and their learning decreases. New instruments are needed to facilitate the learning of the students and the evaluation process are needed. Therefore, in this paper we proposed a new automated scheduling tool, based on graph theory, to fill this gap. The tool employs graph coloring algorithms to calculate all possible schedules for evaluation activities. Students may freely choose the schedule that best fits their personal situation, learning progress, background, etc. using a web portal where all solutions from the coloring algorithm are displayed. This tool was deployed in the Computer Engineering degree at Universidad Politécnica de Madrid, Spain. A pilot experience was conducted for three years (2020-2022) in the context of a cybersecurity course to analyze how this new tool improves the learning and the evaluation process of students. Results show both academic results and the students’ and professor’ satisfaction improve in a significant manner.

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Large-scale timetabling problems with adaptive tabu search

Cited by 11 publications

References 20 publications

Efficient Heuristics for Timetabling Scheduling in Blended Learning

Efficient Heuristics for Timetabling Scheduling in Blended Learning

Hybrid Erosion And Deposition Metaheuristic University Teaching Timetabling Framework

Automated Activity Scheduling Tools for Improving Learning and Evaluation of Cybersecurity Competencies in Computer Engineering Courses

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