Implementation of a university course and examination timetabling system

Dimopoulou, Maria; Miliotis, P.

doi:10.1016/s0377-2217(00)00052-7

Cited by 101 publications

(53 citation statements)

References 29 publications

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“…However, thanks to strong advances in computer software and hardware, and in IP formulations, mathematical programming approaches for timetabling problems have become more popular ( [1], [23]). Examples of IP formulations for UCTPs can be found in [24], [1], [25] and [26]. One advantage of mathematical programming approaches is the ease of incorporating additional soft constraints ( [2]).…”

Section: Solution Techniquesmentioning

confidence: 99%

“…De Causmaecker et al [16] use a decomposed metaheuristic approach to solve the timetabling problem for the KaHo Sint-Lieven School of Engineering. Dimopoulo and Miliotis [24] report on the implementation of a computer system for the joint development of a course and examination timetable at The Athens University of Economics and Business. Schimmelpfeng and Helber [26] describe the implementation of an integer programming approach to create a complete timetable of all courses for a term at the School of Economics and Management at Hannover University.…”

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%

Section: Solution Techniquesmentioning

confidence: 99%

Developing compact course timetables with optimized student flows

Vermuyten

Lemmens

Marqués

et al. 2016

European Journal of Operational Research

View full text Add to dashboard Cite

show abstract

“…The course timetabling problem usually deals with effective distribution of resources where the resources are lecturers, student, courses and particular rooms to a fixed time period. Apart from course timetabling, examination timetabling [3] is also employed in the university. Both course and examination timetabling are seamlessly integrating timetables to reduce conflicts of students and allotment of classes, which can be distinguished according to convenient timing and matters.…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimisation for solving university course timetabling problems

Mazlan¹,

Makhtar²,

Mohamed³

et al. 2018

IJET

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Course timetabling is one of the most important activities faced by any educational institution. Furthermore, the course timetabling process is time-consuming and tiresome as it needs to be prepared for each regular semester. This paper aims to apply the Ant Colony Optimisation (ACO) method to solve the course timetabling problem. This approach is to optimise the properties of the course requirement and minimise various conflicts for the time slot assignation. This method is based on the life of the ant colony in generating automatic timetabling according to the properties (pheromones) such as time, student, lecturer and room, besides satisfying the constraints. The implementation of this method is to find an effective and better solution for university course timetabling. The result and performance evaluation is used to determine whether it is reliable in providing the feasible timetable.

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“…They modeled the basic requirements of academic timetables such as the number of teaching sessions required per week for each course (completeness constraint) and the need for avoiding simultaneous session assignments for students and teachers (uniqueness constraint). These models were later improved and applied by many others including Dimopoulou and Miliotis (2001), Martin (2004) and Boland et al (2006). Daskalaki et al (2004) presented a comprehensive IP formulation for university timetabling including a wide range of applicable constraints such as collision, completeness, consecutive sessions, repetitive sessions, and pre-allocations.…”

Section: Introductionmentioning

confidence: 99%

An Integer Programming Model for a Complex University Timetabling Problem: A Case Study

Prabodanie¹

2017

Industrial Engineering and Management Systems

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A binary integer programming model is proposed for a complex timetabling problem in a university faculty which conducts various degree programs. The decision variables are defined with fewer dimensions to economize the model size of large scale problems and to improve modeling efficiency. Binary matrices are used to incorporate the relationships between the courses and students, and the courses and teachers. The model includes generally applicable constraints such as completeness, uniqueness, and consecutiveness; and case specific constraints. The model was coded and solved using Open Solver which is an open-source optimizer available as an Excel add-in. The results indicate that complicated timetabling problems with large numbers of courses and student groups can be formulated more efficiently with fewer numbers of variables and constraints using the proposed modeling framework. The model could effectively generate timetables with a significantly lower number of work hours per week compared to currently used timetables. The model results indicate that the particular timetabling problem is bounded by the student overlaps, and both human and physical resource constraints are insignificant.

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Implementation of a university course and examination timetabling system

Cited by 101 publications

References 29 publications

Developing compact course timetables with optimized student flows

Developing compact course timetables with optimized student flows

Ant colony optimisation for solving university course timetabling problems

An Integer Programming Model for a Complex University Timetabling Problem: A Case Study

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