The preparation of examination time-tables using a small-store computer

Cole, Ann

doi:10.1093/comjnl/7.2.117

Cited by 44 publications

(10 citation statements)

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“…The initial studies on computer based strategies for examination timetabling date back to the 1960s (Cole 1964;Broder 1964). Research interest in examination timetabling has been progressively increasing since then.…”

Section: An Overview Of Approaches To Examination Timetablingmentioning

confidence: 99%

Monte Carlo hyper-heuristics for examination timetabling

et al. 2010

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Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyper-heuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given domain, but a key goal is also to extend the level of generality so that different problems from different domains can also be solved. Indeed, a major challenge is to explore how the heuristic design process might be automated. Almost all existing iterative selection hyper-heuristics performing single point search contain two successive stages; heuristic selection and move acceptance. Different operators can be used in either of the stages. Recent studies explore ways of introducing learning mechanisms into the search process for improving the performance of hyper-heuristics. In this study, a broad empirical analysis is performed comparing Monte Carlo based hyper-heuristics for solving capacitated examination timetabling problems. One of these hyper-heuristics is an approach that overlaps two stages and presents them in a single algorithmic body. A learning heuristic selection method (L) operates in harmony with a simulated annealing move acceptance method using reheating (SA) based on some shared variables. Yet, the heuristic selection and move acceptance methods can be separated as the proposed approach respects the common selection hyper-heuristic framework. The experimental results show that simulated annealing with reheating as a hyper-heuristic move acceptance method has significant potential. On the other hand, the learning hyper-heuristic using simulated annealing with reheating move acceptance (L-SA) performs poorly due to certain weaknesses, such as the choice of rewarding mechanism and the evaluation of utility values for heuristic selection as compared to some other hyper-heuristics in examination timetabling. Trials with other heuristic selection methods confirm that the best alternative for the simulated annealing with reheating move acceptance for examination timetabling is a previously proposed strategy known as the choice function

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Section: An Overview Of Approaches To Examination Timetablingmentioning

confidence: 99%

Monte Carlo hyper-heuristics for examination timetabling

et al. 2010

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“…The sequential constructive algorithm is amongst the earliest approaches that has been used to tackle the examination timetabling problem in an automated way [28][29][30]. In this approach, the concept of 'failed first' is implemented.…”

Section: The Basic Sequential Constructive Algorithmmentioning

confidence: 99%

An investigation of fuzzy multiple heuristic orderings in the construction of university examination timetables

Asmuni

Burke

Garibaldi

et al. 2009

Computers & Operations Research

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In this paper, we present an investigation into using fuzzy methodologies to guide the construction of high quality feasible examination timetabling solutions. The provision of automated solutions to the examination timetabling problem is achieved through a combination of construction and improvement. The enhancement of solutions through the use of techniques such as metaheuristics is, in some cases, dependent on the quality of the solution obtained during the construction process. With a few notable exceptions, recent research has concentrated on the improvement of solutions as opposed to focusing on investigating the 'best' approaches to the construction phase. Addressing this issue, our approach is based on combining multiple criteria in deciding on how the construction phase should proceed. Fuzzy methods were used to combine three single construction heuristics into three different pair wise combinations of heuristics in order to guide the order in which exams were selected to be inserted into the timetable solution. In order to investigate the approach, we compared the performance of the various heuristic approaches with respect to a number of important criteria (overall cost penalty, number of skipped exams, number of iterations of a rescheduling procedure required and computational time) on twelve well-known benchmark problems. We demonstrate that the fuzzy combination of heuristics allows high quality solutions to be constructed. On one of the twelve problems we obtained lower penalty than any previously published constructive method and for all twelve we obtained lower penalty than when any of the single heuristics were used alone. Furthermore, we demonstrate that the fuzzy approach used less backtracking when constructing solutions than any of the single heuristics. We conclude that this novel fuzzy approach is a highly effective method for heuristically constructing solutions and, as such, has particular relevance to real- Preprint submitted to Elsevier 29 April 2008world situations in which the construction of feasible solutions is often a difficult task in its own right.

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“…For example, the timeslots and rooms are allocated to each event in an ordered list of pairs like: (2, 4), (3,30), (1,12), · · · , (2, 7), where timeslot 4 and room 2 are allocated to event 1, timeslot 30 and room 3 are allocated to event 2, and so on.…”

Section: The University Course Timetabling Problemmentioning

confidence: 99%

“…Thereafter, programs were soon presented with the aim of fitting classes and teachers to periods. In 1964, Broder [6] and Cole [12] both presented heuristic approaches to timetabling. In 1967, Welsh and Powell [26] pointed out the similarity between the timetabling problem and the one of coloring the vertices of a graph.…”

Section: Introductionmentioning

confidence: 99%