An effective hybrid algorithm for university course timetabling

Chiarandini, Marco; Birattari, Mauro; Socha, Krzysztof; Rossi-Doria, Olivia

doi:10.1007/s10951-006-8495-8

Cited by 115 publications

(92 citation statements)

References 34 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Three approaches perform better in terms of solution quality. In fact, another hybrid algorithm due to Chiarandini et al (2006) would have won the competition but could not enter because they were the organisers (see their results in table 4). This is not surprising since these bespoke approaches were specifically tailored for these problem instances and heavily utilised some of features of these instances while our method was designed with generality in mind.…”

Section: Computational Resultsmentioning

confidence: 99%

A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

View full text Add to dashboard Cite

Abstract. One of the main motivations for investigating hyper-heuristic methodologies is to provide a more general search framework than is currently available. Most of the current search techniques represent approaches that are largely adapted for specific search problems (and, in some cases, even specific problem instances). There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are more generally applicable to more problems. One of our motivating goals is to underpin the development of more flexible search methodologies that can be easily and automatically employed on a broader range of problems than is currently possible. Almost all the heuristics that have appeared in the literature have been designed and selected by humans. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark data sets drawn from three very different and difficult (NP-hard) problems: nurse rostering, university course timetabling and onedimensional bin packing. Experimental results show that the proposed hyper-heuristic is able to achieve significant performance improvements over a recently proposed tabu search hyper-heuristic without lowering the level of generality. We also show that our hyper-heuristic is capable of producing competitive results against bespoke meta-heuristics methods for these problems. In some cases, the simulated annealing hyper-heuristic has even obtained considerable improvements over some of the current best problem-specific meta-heuristic approaches. The contribution of this paper is to present a method which can be readily (and automatically) applied to very different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.

show abstract

Section: Computational Resultsmentioning

confidence: 99%

A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

View full text Add to dashboard Cite

show abstract

“…To exclude non-correlated disturbances, we turned off some strategies that are able to improve the algorithm's performance. Therefore, the results presented in this paper are not as good as those reported in [24], [28]. For example, we used signi cantly less iterations than in [24] which, if allowed, will signi cantly improve our results.…”

Section: Dynamic Memory Lengthmentioning

confidence: 59%

“…Therefore, the results presented in this paper are not as good as those reported in [24], [28]. For example, we used signi cantly less iterations than in [24] which, if allowed, will signi cantly improve our results. Also, introducing a reheating strategy that is similar to [19] has the potential to improve the results.…”

Section: Dynamic Memory Lengthmentioning

confidence: 59%

See 1 more Smart Citation

Memory Length in Hyper-heuristics: An Empirical Study

Bai

Burke

Gendreau

et al. 2007

2007 IEEE Symposium on Computational Intelligence in Scheduling

View full text Add to dashboard Cite

Abstract-Hyper-heuristics are an emergent optimisation methodology which aims to give a higher level of flexibility and domain-independence than is currently possible. Hyper-heuristics are able to adapt to the different problems or problem instances by dynamically choosing between heuristics during the search. This paper is concerned with the issues of memory length on the performance of hyper-heuristics. We focus on a recently proposed simulated annealing hyper-heuristic and choose a set of hard university course timetabling problems as the test bed for this empirical study. The experimental results show that the memory length can affect the performance of hyper-heuristics and a good choice of memory length is able to improve solution quality. Finally, two dynamic approaches are investigated and one of the approaches is shown to be able to produce promising results without introducing extra sensitive algorithmic parameters.

show abstract

“…e.g. Chiarandini et al [2006], Pellegrini and Birattari [2007], Kristiansen et al [2013]). The drawbacks of F-Race are that all possible parameter con gurations are considered.…”

Section: Parameter Tuningmentioning

confidence: 99%

Elective course student sectioning at Danish high schools

Kristiansen

Stidsen

2014

Ann Oper Res

View full text Add to dashboard Cite

The Elective Course Student Sectioning (ECSS) problem is an yearly recurrent planning problem at the Danish high schools. The problem is of assigning students to elective course classes given their requests such that as many requests are ful lled and the violation of the soft constraints is minimized. This paper presents an adaptive large neighborhood search heuristic for the ESCC. The algorithm is applied to 80 real-life instances from Danish high schools and compared with solutions found by using the state-of-the-art MIP solver Gurobi. The algorithm has been implemented in the commercial product Lectio, and is thereby available for approximately 200 high schools in Denmark.

show abstract

An effective hybrid algorithm for university course timetabling

Cited by 115 publications

References 34 publications

A simulated annealing hyper-heuristic methodology for flexible decision support

A simulated annealing hyper-heuristic methodology for flexible decision support

Memory Length in Hyper-heuristics: An Empirical Study

Elective course student sectioning at Danish high schools

Contact Info

Product

Resources

About