Lower bounds for the ITC-2007 curriculum-based course timetabling problem

Hao, Jin‐Kao; Benlic, Una

doi:10.1016/j.ejor.2011.02.019

Cited by 26 publications

(23 citation statements)

References 15 publications

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“…Asín Achá et al [50] give an application of several satisfiability solvers, where (by using different encodings) they were able to compute and obtain new best solutions. Recently, Cacchiani [51] computed new best solutions using an approach similar to [49]. However in this case the partition is based on soft constraints instead of hard constraints.…”

Section: Track 2 -Post-enrollment Course Timetablingmentioning

confidence: 99%

“…The first stage builds an initial feasible timetable through a fast heuristic, then intensification and diversification phases are alternatively applied using tabu search to reduce soft constraints violations. In Hao et al [49] a partition-based approach is used to compute new lower bounds for theses instances. This divide and conquer approach uses iterative tabu search to partition the initial problem into sub-problems which are solved with an ILP solver.…”

Section: Track 2 -Post-enrollment Course Timetablingmentioning

confidence: 99%

See 1 more Smart Citation

Iterated local search using an add and delete hyper-heuristic for university course timetabling

Soria-Alcaraz

Özcan

Swan

et al. 2016

Applied Soft Computing

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractHyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated Local Search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyperheuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyperheuristic approach.

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Section: Track 2 -Post-enrollment Course Timetablingmentioning

confidence: 99%

Section: Track 2 -Post-enrollment Course Timetablingmentioning

confidence: 99%

Iterated local search using an add and delete hyper-heuristic for university course timetabling

Soria-Alcaraz

Özcan

Swan

et al. 2016

Applied Soft Computing

View full text Add to dashboard Cite

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“…Al-Yakoob and Sherali [18] and MirHassani [19] also proposed mixed integer models to prepare timetable for universities in Kuwait and Iran respectively. Hao and Benlic [21] suggested a linear integer model that uses tabu-search techniques and similar to approaches that dividing main problem to sub-problems. There have been several studies that developed a timetable model for the universities in Turkey.…”

Section: Literature Reviewmentioning

confidence: 99%

COTTAPP: An Online University Timetable Application based on a Goal Programming Model

Dursun¹,

Su²,

Cosgun³

et al. 2017

ijisae

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Preparing university course timetables is a challenging task as many constraints and requirements from the university and lecturers must be satisfied without overlapping courses for different student groups. Although many mathematical optimization models have been proposed to automate this task, a wider use of these models have been limited as deep technical understanding of mathematical and computer programming are required in order to use and implement them. This paper proposes a simple and flexible course timetabling application that is based on a weighted binary goal programming model with a powerful solver. Our application enables the users to modify and run this model by using a simple web and spreadsheet interface. Consequently, the model does not require deep technical understanding of the underlying models from its users even though it is based on a complex mathematical model. The web application and the underlying optimization model is illustrated by using a case study of an undergraduate program of industrial engineering.

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“…The authors applied their algorithm on instances based on the datasets from the first international timetabling competition (ITC2002). Hao and Benlic [39] combine tabu search and IP for finding new lower bounds for the ITC2007 curriculum based course timetabling problem. Phillips et al [25] validate their IP model for solving a UCTP through a real-life case at the University of Auckland and on instances from the ITC2007.…”

Section: Outline Of the Current Articlementioning

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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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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Lower bounds for the ITC-2007 curriculum-based course timetabling problem

Cited by 26 publications

References 15 publications

Iterated local search using an add and delete hyper-heuristic for university course timetabling

Iterated local search using an add and delete hyper-heuristic for university course timetabling

COTTAPP: An Online University Timetable Application based on a Goal Programming Model

Developing compact course timetables with optimized student flows

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