A survey of approaches for university course timetabling problem

Babaei, Hamed; Karimpour, Jaber; Hadidi, Amin

doi:10.1016/j.cie.2014.11.010

Cited by 182 publications

(120 citation statements)

References 36 publications

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“…Overviews were given by Carter and Laporte ( [5], [2]), Schaerf [6], Burke and Petrovic [7], Petrovic and Burke [8], Lewis [9], MirHassani and Habibi [10] and Babaei et al [11]. Below, we discuss three approaches that are most widely used for course timetabling in more detail, namely graph coloring, metaheuristic approaches, and mathematical programming.…”

Section: Solution Techniquesmentioning

confidence: 99%

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Developing compact course timetables with optimized student flows

Vermuyten

Lemmens

Marqués

et al. 2016

European Journal of Operational Research

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In university buildings with many rooms spread over different floors, large student flows between two consecutive lectures might cause congestion problems. These congestions result in long queues at elevators or at stairwells, which might lead to delays in lecture starts. The course timetable clearly has an important impact on these congestions. This paper presents a two-stage integer programming approach for building a university course timetable that aims at minimizing the resulting student flows. The first stage minimizes the violation of the teacher and educational preferences by assigning lectures to timeslots and rooms. The second stage reassigns classrooms to lectures of the timetable of the first stage and minimizes the student flow. The conceptual model is applied to the dataset of the Faculty of Economics and Business of the KU Leuven Campus Brussels and is tested and validated with 21 adapted instances from the literature. In contrast to a monolithic model, the two-stage model consistently succeeds in finding good quality feasible solutions. Moreover, the generated timetables entail significantly reduced student flows compared to the flows of the manually developed course timetable.

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Section: Solution Techniquesmentioning

confidence: 99%

“…Constraint set (11) shows how this prohibition is enforced. Finally, the legislator does not allow that a docent teaches in the first and last timeslot of a particular day.…”

Section: ∀L ∈ Lmentioning

confidence: 99%

Developing compact course timetables with optimized student flows

Vermuyten

Lemmens

Marqués

et al. 2016

European Journal of Operational Research

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“…2) Finding constraint each taboo list ( ) Constraint each taboo list ( ) is calculated from the constraints obtained by each ant with the following formula by equation (2)…”

Section: ) Initialization Parametersmentioning

confidence: 99%

“…The scheduling process should be carried out in every turn of the semester, it makes this work is exhausting and time-consuming. In a scheduling problem at the university each restriction should not be violated [2].…”

Section: Introductionmentioning

confidence: 99%

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

Ashari

Muslim

Alamsyah

2016

SJI

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Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.

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“…Different approaches can also be found in the literature concerning other timetabling problems, including operational research, metaheuristics, or novel intelligent methods, such as university course timetabling problems [12], job shop scheduling problems [13][14][15], and sports scheduling problems [16].…”

Section: Introductionmentioning

confidence: 99%

The Air Traffic Controller Work-Shift Scheduling Problem in Spain from a Multiobjective Perspective: A Metaheuristic and Regular Expression-Based Approach

Tello

Mateos

Jiménez-Martín

et al. 2018

Mathematical Problems in Engineering

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We address an air traffic control operator (ATCo) work-shift scheduling problem. We consider a multiple objective perspective where the number of ATCos is fixed in advance and a set of ATCo labor conditions have to be satisfied. The objectives deal with the ATCo work and rest periods and positions, the structure of the solution, the number of control center changes, or the distribution of the ATCo workloads. We propose a three-phase problem-solving methodology. In the first phase, a heuristic is used to derive infeasible initial solutions on the basis of templates. Then, a multiple independent run of the simulated annealing metaheuristic is conducted aimed at reaching feasible solutions in the second phase. Finally, a multiple independent simulated annealing run is again conducted from the initial feasible solutions to optimize the objective functions. To do this, we transform the multiple to single optimization problem by using the rank-order centroid function. In the search processes in phases 2 and 3, we use regular expressions to check the ATCo labor conditions in the visited solutions. This provides high testing speed. The proposed approach is illustrated using a real example, and the optimal solution which is reached outperforms an existing template-based reference solution.

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A survey of approaches for university course timetabling problem

Cited by 182 publications

References 36 publications

Developing compact course timetables with optimized student flows

Developing compact course timetables with optimized student flows

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

The Air Traffic Controller Work-Shift Scheduling Problem in Spain from a Multiobjective Perspective: A Metaheuristic and Regular Expression-Based Approach

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