Examination timetabling in British Universities: A survey

Burke, Edmund K.; Elliman, Dave; Ford, Peter; Weare, Rupert F.

doi:10.1007/3-540-61794-9_52

Cited by 90 publications

(63 citation statements)

References 2 publications

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“…This problem can be viewed as an extension of the Graph Coloring Problem, with a high number of additional soft and hard constraints. Although there are different versions of the specification of the Exam Timetabling Problem (representing different institutional requirements, see (Burke et al 1996)) the most common hard constraint is that no one student should sit two exams at the same time. Other hard constraints specify room availability, matching the durations of exams and corresponding timeslots.…”

Section: Benchmark Problemsmentioning

confidence: 99%

The late acceptance Hill-Climbing heuristic

Burke

Bykov

2017

European Journal of Operational Research

146

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This paper introduces a new and very simple search methodology called Late Acceptance Hill-Climbing (LAHC). It is a one-point iterative search algorithm, which accepts non-improving moves when a candidate cost function is better (or equal) than it was a number of iterations before. This value appears as a single algorithmic input parameter which determines the total processing time of the search procedure. The properties of this method are experimentally studied in this paper with a range of Travelling Salesman and Exam Timetabling benchmark problems. In addition, we present a fair comparison of LAHC with well-known search techniques, which employ different variants of a cooling schedule: Simulated Annealing (SA), Threshold Accepting (TA) and the Great Deluge Algorithm (GDA). Moreover, we discuss the method's success in winning an international competition to automatically solve the Magic Square problem. Our experiments have shown that the LAHC approach is simple, easy to implement and yet is an effective search procedure. For all studied problems, its average performance was distinctly better than GDA and on the same level as SA and TA. One of the major advantages of LAHC approach is the absence of a cooling schedule. This makes it significantly more robust than cooling-schedule based techniques. We present an example where the rescaling of a cost function in the Exam Timetabling Problem dramatically deteriorates the performance of three cooling-schedule based techniques, but has absolutely no influence upon the performance of LAHC.

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Section: Benchmark Problemsmentioning

confidence: 99%

The late acceptance Hill-Climbing heuristic

Burke

Bykov

2017

European Journal of Operational Research

146

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“…Often the algorithms are developed for a narrower set of problems within that group, for instance university course timetabling [12] or exam timetabling problems [13]. Indeed, algorithms can be specialized further by developing them for a specific organization, whose timetabling problem may have a structure very different to that of another organization with different resources and constraints [14]. At each of these levels, the use of domain knowledge can allow the algorithms to exploit the structure of the set of problems in question.…”

Section: Motivation For This Research Areamentioning

confidence: 99%

A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics

Burke

Hyde

Kendall

et al. 2010

IEEE Trans. Evol. Computat.

120

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract-We present a genetic programming (GP) system to evolve reusable heuristics for the 2-D strip packing problem. The evolved heuristics are constructive, and decide both which piece to pack next and where to place that piece, given the current partial solution. This paper contributes to a growing research area that represents a paradigm shift in search methodologies. Instead of using evolutionary computation to search a space of solutions, we employ it to search a space of heuristics for the problem. A key motivation is to investigate methods to automate the heuristic design process. It has been stated in the literature that humans are very good at identifying good building blocks for solution methods. However, the task of intelligently searching through all of the potential combinations of these components is better suited to a computer. With such tools at their disposal, heuristic designers are then free to commit more of their time to the creative process of determining good components, while the computer takes on some of the design process by intelligently combining these components. This paper shows that a GP hyperheuristic can be employed to automatically generate human competitive heuristics in a very-well studied problem domain.

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“…Such uncapacitated data is useful for developing and proving the methodology but most real-world problems will have a limited set of rooms of varying capacities available. In reality, different institutions must satisfy a range of different constraints in generating an institution-wide timetable [19].…”

Section: Application To Of the Methodology To Capacitated Datamentioning

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 timetabling in British Universities: A survey

Cited by 90 publications

References 2 publications

The late acceptance Hill-Climbing heuristic

The late acceptance Hill-Climbing heuristic

A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics

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

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