Examination Timetables and Tabu Search with Longer-Term Memory

White, George M.; Xie, Bill S.

doi:10.1007/3-540-44629-x_6

Cited by 42 publications

(29 citation statements)

References 7 publications

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“…We believe that TEMCQ could also be applied to other problem domains. Burke & Newall [5] Merlot et al [7] Kendall & Hussin [9] Asmuni et al [16] White et al [31] Burke et al [17] Burke et al [4] Burke et al [21] Abdullah et al [26] Abdullah et al [20] TEMCQ …”

Section: Discussionmentioning

confidence: 99%

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Tabu exponential Monte-Carlo with counter heuristic for examination timetabling

Sabar

Ayob

Kendall

2009

2009 IEEE Symposium on Computational Intelligence in Scheduling

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Section: Discussionmentioning

confidence: 99%

“…In 2001White and Xie [31] developed a four-stage Tabu Search called OTTABU. Using an exam timetabling problem from the University of Ottawa, their approach showed that solutions could be gradually improved by considering a greater number of constraints at each stage of the process.…”

Section: Introductionmentioning

confidence: 99%

Tabu exponential Monte-Carlo with counter heuristic for examination timetabling

Sabar

Ayob

Kendall

2009

2009 IEEE Symposium on Computational Intelligence in Scheduling

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“…P4: Burke and Newall (2003): Enhancing timetable solutions with local search methods P5: Merlot et al (2003): A hybrid algorithm. P6: White et al (2001): Tabu search with longer-term memory P7: Burke et al (2007): A graph-based hyper-heuristic. P8: Abdullah et al (2006): A multi-start large neighbourhood search approach with local search methods.…”

Section: The Hbmo-etp For the Carter Uncapacitated Exam Benchmark Datmentioning

confidence: 99%

A honey-bee mating optimization algorithm for educational timetabling problems

Sabar

Ayob

Kendall

et al. 2012

European Journal of Operational Research

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Abstract:In this work, we propose a variant of the Honey-bee Mating Optimization Algorithm for solving educational timetabling problems. The honey-bee algorithm is a nature inspired algorithm which simulates the process of real honey-bees mating. The performance of the proposed algorithm is tested over two benchmark problems; exam (Carter's un-capacitated datasets) and course (Socha datasets) timetabling problems. We chose these two datasets as they have been widely studied in the literature and we would also like to evaluate our algorithm across two different, yet related, domains. Results demonstrate that the performance of the honey-bee mating optimization algorithm is comparable with the results of other approaches in the scientific literature. Indeed, the proposed approach obtains best results compared with other approaches on some instances, indicating that the honey-bee mating optimization algorithm is a promising approach in solving educational timetabling problems.

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“…Subsequently, metaheuristic approaches have been used to help improve the solution. Simulated annealing [4], Tabu search [5] and other techniques such a Great Deluge [6] and Memetic Algorithms [7] have all proved useful. In general metaheuristic approaches have performed well on benchmark problems though they are time consuming compared with graph colouring based approaches.…”

Section: Introductionmentioning

confidence: 99%

A New Neural Network Based Construction Heuristic for the Examination Timetabling Problem

Corr

McCollum

McGreevy

et al. 2006

Parallel Problem Solving From Nature - PPSN IX

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Abstract. This paper examines the application of neural networks as a construction heuristic for the examination timetabling problem. Building on the heuristic ordering technique, where events are ordered by decreasing scheduling difficulty, the neural network allows a novel dynamic, multi-criteria approach to be developed. The difficulty of each event to be scheduled is assessed on several characteristics, removing the dependence of an ordering based on a single heuristic. Furthermore, this technique allows the ordering to be reviewed and modified as each event is scheduled; a necessary step since the timetable and constraints are altered as events are placed. Our approach uses a Kohonen self organising neural network and is shown to have wide applicability. Results are presented for a range of examination timetabling problems using standard benchmark datasets.

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Examination Timetables and Tabu Search with Longer-Term Memory

Cited by 42 publications

References 7 publications

Tabu exponential Monte-Carlo with counter heuristic for examination timetabling

Tabu exponential Monte-Carlo with counter heuristic for examination timetabling

A honey-bee mating optimization algorithm for educational timetabling problems

A New Neural Network Based Construction Heuristic for the Examination Timetabling Problem

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