Benchmarking curriculum-based course timetabling: formulations, data formats, instances, validation, visualization, and results

Bonutti, Alex; Cesco, Fabio De; Gaspero, Luca Di; Schaerf, Andrea

doi:10.1007/s10479-010-0707-0

Cited by 65 publications

(58 citation statements)

References 20 publications

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“…As a potential example, the structure of the PSTP is similar to those used in the ITC2007 course timetabling track, [35], and so this suggests that ICMA be also tested on this domain. We note that [55] uses an integer programming method (as opposed to a metaheuristic) but that it does exploit the given grouping structure of the events in the problem; thus re-iterating the general potential utility of structure exploitation.…”

Section: Resultsmentioning

confidence: 99%

“…Two tracks of this competition covered two different CTPs: post enrollment course timetabling and curriculum based course timetabling and a third track covered the examination timetabling problem. Bonutti et al [35] describe formulations for the curriculum based course timetabling problem introducing new problem instances additional to the ones provided in the competition. The best performing curriculum based course timetabling and examination timetabling approach turned out to be a multi-stage hybrid algorithm as described in Müller (2009) [36].…”

Section: High-school Course Timetabling and Metaheuristic Solutionsmentioning

confidence: 99%

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The Interleaved Constructive Memetic Algorithm and its application to timetabling

Özcan

Parkes

Alkan

2012

Computers & Operations Research

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Timetabling problems are well known NP-hard constraint satisfaction, and real-world cases often have complicated and challenging structures. For such problems, we present a new hybrid method, "Interleaved Constructive Memetic Algorithm" (ICMA) that interleaves memetic algorithms with constructive methods. ICMA works using an active subset of all the events. Starting with a few events, in multiple construction stages ICMA increases the active set to eventually include all of them. At each stage, a memetic algorithm (MA) is applied to improve the current partial solution before the next construction step. We also describe a real-world course timetabling problem, the "Preparation School Timetabling Problem" (PSTP), which is of particular interest because it has a highly hierarchical structure arising from various organisational requirements. An important advantage of ICMA is that both the constructive heuristics and MA can be tailored to exploit such hierarchical structures. We give empirical results showing that ICMA performs better than the corresponding conventional MA on the PSTP.

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Section: Resultsmentioning

confidence: 99%

Section: High-school Course Timetabling and Metaheuristic Solutionsmentioning

confidence: 99%

The Interleaved Constructive Memetic Algorithm and its application to timetabling

Özcan

Parkes

Alkan

2012

Computers & Operations Research

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“…The set of heuristics are supposed to make some progressive moves in datasets. This article is expansion of research work published by [1] and shown the functionally of four more important low level heuristics (LLH 8 , LLH 9 , LLH 10 , LLH 11 ).…”

Section: Introductionmentioning

confidence: 90%

Formulation of low level heuristics

Shaikh

Raziq

Muhammad

2014

IJBAS

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The curricula scheduling is very significant and largely studied problem in academia. The desired solution calculatedly assembles the academic events over the carefully designed layout considering several predefined interlinked constraints. The contemporary research for solving scheduling constraints is inclined to raise the degree of generality, so that a wide range of identical problems may be addressed. The hyper-heuristic is such a state-of-the-art solving technique which stands on multi-layered framework. The top layer usually consists of classic algorithm for managing the operators on down-layers, and the same is occasionally assisted by machine learning or similar techniques. This research article examines the performance of the small group of bespoke low level heuristics. These LLHs are operated by hyperheuristic to address the specific scheduling constraints. The set of heuristics are divided into a range of subgroups including timescale category which contain two subsets Day and Period. The utility group which contains two patterns named Shift and Swap techniques, while the third category encircles three more subgroups of Random or Sami-Random and Progressive.

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“…These are real cases taken mainly from the University of Udine which were used for the International Timetabling Competition in 2007-08 (ITC2007) [47]. Since the objective of the timetabling problem of the FEB Campus Brussels is novel in the literature (minimization of the student flow between classrooms), we do not intend to Table 3: Description of the instances tested.…”

Section: Data From the Literaturementioning

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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Benchmarking curriculum-based course timetabling: formulations, data formats, instances, validation, visualization, and results

Cited by 65 publications

References 20 publications

The Interleaved Constructive Memetic Algorithm and its application to timetabling

The Interleaved Constructive Memetic Algorithm and its application to timetabling

Formulation of low level heuristics

Developing compact course timetables with optimized student flows

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