Recent research directions in automated timetabling

Burke, Edmund K.; Petrović, Sanja

doi:10.1016/s0377-2217(02)00069-3

Cited by 320 publications

(192 citation statements)

References 18 publications

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“…Creating a feasible timetable can be done in a number of ways which can be grouped as following [1], [3], [4], [12]. 1) Graph coloring 2) Clustering methods 3) Constraint based methods (e. g. integer programming) 4) Meta heuristic methods (e. g. genetic algorithms, simulated annealing, Tabu search and ants colony) Most of the approaches have limited the constraints to gain a quick and good result.…”

Section: Related Workmentioning

confidence: 99%

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Timetabling: A State-of-the-Art Evolutionary Approach

Doulaty¹,

Derakhshi²,

Abdi³

2013

IJMLC

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Abstract-Timetabling is the task of creating a schedule while satisfying some constraints. This problem is an NPComplete problem, so solving it needs some heuristics. There are many types of timetabling; we mainly focused on university course timetabling. There have been many efforts in literature, but most of them have used some limiting assumptions that cause their approach to be unusable in real situations. We've used an evolutionary approach based on genetic algorithm to solve this problem in real situation and in a reasonable amount of time. We've used real data from our department in the university and our approach could solve the problem in about 15 minutes, while manually timetabling may take three days.

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Section: Related Workmentioning

confidence: 99%

“…Furthermore, most of the existing approaches only have a minimal set of soft constraints (this is mainly caused by the fact that they wanted to have an easy to implement and quick solution [3], [12]. For those which does not have a minimal set of soft constraints and used genetic approach, this problem is mainly related to their Fitness function [3].…”

mentioning

confidence: 99%

Timetabling: A State-of-the-Art Evolutionary Approach

Doulaty¹,

Derakhshi²,

Abdi³

2013

IJMLC

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“…A concise overview of some general trends in automated timetabling can be found in [1][2][3][4][5]. Many university course timetabling problems in literature have been intentionally simplified, since real-world examples hold numerous features that make algorithm implementation and performance tracking complicated.…”

Section: Introductionmentioning

confidence: 99%

University Course Timetabling with Genetic Algorithm: A Laboratory Excercises Case Study

Bratković

Herman

Omrčen

et al. 2009

Lecture Notes in Computer Science

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Abstract. This paper describes the application of a hybrid genetic algorithm to a real-world instance of the university course timetabling problem. We address the timetabling of laboratory exercises in a highly constrained environment, for which a formal definition is given. Solution representation technique appropriate to the problem is defined, along with associated genetic operators and a local search algorithm. The approach presented in the paper has been successfully used for timetabling at the authors' institution and it was capable of generating timetables for complex problem instances.

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“…A detailed discussion of these techniques is given by Carter et al [2] and an excellent review of the field is provided by Burke et al [3].…”

Section: Introductionmentioning

confidence: 99%

“…Early approaches include techniques such as Graph Colouring and Constraint Programming [3]. Subsequently, metaheuristic approaches have been used to help improve the solution.…”

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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Recent research directions in automated timetabling

Cited by 320 publications

References 18 publications

Timetabling: A State-of-the-Art Evolutionary Approach

Timetabling: A State-of-the-Art Evolutionary Approach

University Course Timetabling with Genetic Algorithm: A Laboratory Excercises Case Study

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

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