Linear combinations of heuristics for examination timetabling

Burke, Edmund K.; Pham, Nam Ngoc; Qu, Rong; Yellen, Jay

doi:10.1007/s10479-011-0854-y

Cited by 22 publications

(9 citation statements)

References 30 publications

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“…One of the most successful implementations of hyperheuristics is in timetabling problems, in particular examination timetabling. Most recently published studies on examination timetabling problems with hyperheuristics are discussed in [17,20,[24][25][26][27][28][29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Fairness in examination timetabling: Student preferences and extended formulations

Muklason

Parkes

zcan

et al. 2017

Applied Soft Computing

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Variations of the examination timetabling problem have been investigated by the research community for more than two decades. The common characteristic between all problems is the fact that the definitions and data sets used all originate from actual educational institutions, particularly universities, including specific examination criteria and the students involved. Although much has been achieved and published on the state-of-the-art problem modelling and optimisation, a lack of attention has been focussed on the students involved in the process. This work presents and utilises the results of an extensive survey seeking student preferences with regard to their individual examination timetables, with the aim of producing solutions which satisfy these preferences while still also satisfying all existing benchmark considerations. The study reveals one of the main concerns relates to fairness within the students cohort; i.e. a student considers fairness with respect to the examination timetables of their immediate peers, as highly important. Considerations such as providing an equitable distribution of preparation time between all student cohort examinations, not just a majority, are used to form a measure of fairness. In order to satisfy this requirement, we propose an extension to the state-of-the-art examination timetabling problem models widely used in the scientific literature. Fairness is introduced as a new objective in addition to the standard objectives, creating a multi-objective problem. Several real-world examination data models are extended and the benchmarks for each are used in experimentation to determine the effectiveness of a multi-stage multi-objective approach based on weighted Tchebyceff scalarisation in improving fairness along with the other objectives. The results show that the proposed model and methods allow for the production * Corresponding author of high quality timetable solutions while also providing a trade-off between the standard soft constraints and a desired fairness for each student.

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Section: Related Workmentioning

confidence: 99%

Fairness in examination timetabling: Student preferences and extended formulations

Muklason

Parkes

zcan

et al. 2017

Applied Soft Computing

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“…Examples of timetabling which combine graph colouring with meta-heuristics (see below) include: Dowsland (1990); Dowsland and Thompson (2005); Burke et al (2012c).…”

Section: C) Examination Scheduling and Course Timetablingmentioning

confidence: 99%

Operational Research in education

Johnes

2015

European Journal of Operational Research

123

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Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions.

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“…(1) The tabu-search developed in [11] (2) The linear combination of ordering criteria by [14] (3) The automated heuristic construction using heuristic hybridisation [30] (4) Four different high-level search techniques based on local search [29] (5) The fuzzy logic system on a pair of ordering criteria in [2] (6) The fuzzy logic system with tuning [2] (7) The extended fuzzy logic system on three ordering criteria [3] (8) The evolutionary algorithm on variable-length sequences [26] (9) The approach that combines heuristics as tie-breakers [27] (10) The genetic programming to evolve functions to order exams [28] From Tables 5 and 6, the EDA-HH has produced promising results over all instances compared to the best results reported in the literature for each of the instances. It also demonstrates high generality over all instances compared to other hyper-heuristic approaches.…”

Section: Comparisons Of Eda-hh Against the Best Approaches In The Litmentioning

confidence: 99%

Hybridising heuristics within an estimation distribution algorithm for examination timetabling

Pham

Bai

et al. 2014

Appl Intell

Self Cite

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This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyperheuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help facilitate more intelligent hyper-heuristic search methods.

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Linear combinations of heuristics for examination timetabling

Cited by 22 publications

References 30 publications

Fairness in examination timetabling: Student preferences and extended formulations

Fairness in examination timetabling: Student preferences and extended formulations

Operational Research in education

Hybridising heuristics within an estimation distribution algorithm for examination timetabling

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