A genetic algorithm approach to school timetabling

Beligiannis, Grigorios N.; Moschopoulos, Charalampos; Likothanassis, Spiridon D.

doi:10.1057/palgrave.jors.2602525

Cited by 40 publications

(26 citation statements)

References 36 publications

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“…The process of constructing a class schedule involves using, among others: simulated annealing [1,35,39], evolutionary algorithms [9], neural network algorithms [13], tabu search heuristics [2,4,5,8,11,12,14,20,30], genetic algorithms [10,18,36], integer programming [6,11,15,16,27,31,33] and constraint programming [19,23,25,37].…”

Section: Literature Overviewmentioning

confidence: 99%

Tabu Search Techniques for Polish Higher Education Timetabling Problems

Milewska¹

2017

dtetr

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This article discusses the problem of creating university class timetables. When formulating the mathematical model, the author took into account the organisational conditions in Poland. He applied heuristic methods when searching for solutions. The model was defined as an ordered string of variables representing: the subject, the lecturer, the student group, the time slot (teaching block) and place of study. The author proposed a three-stage scheme of looking for solutions. Model construction was based on the preferences of lecturers who conduct classes in obligatory and facultative subjects for the student groups organised, and the preferences of students with permission for an individualised course of study.

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Section: Literature Overviewmentioning

confidence: 99%

Tabu Search Techniques for Polish Higher Education Timetabling Problems

Milewska¹

2017

dtetr

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“…Co-teaching restrictions: two or more teachers who teach the same lesson to the same class must be assigned to it at the same time period. For example, one class can be firstly joined with another class and then divided into two sub-classes, one for "English language for beginners" and one for "English language for intermediates" [22]. 7.…”

Section: Constraintsmentioning

confidence: 99%

“…Sub-classes restrictions: two or more teachers who teach different lessons to the same class at the same time period must be simultaneously assigned to it. For example, one class can be divided into two sub-classes, one for "Gymnastics" and one for "Economics" [22].…”

Section: Constraintsmentioning

confidence: 99%

A Comparative Study of Modern Heuristics on the School Timetabling Problem

2015

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Abstract:In this contribution a comparative study of modern heuristics on the school timetabling problem is presented. More precisely, we investigate the application of two population-based algorithms, namely a Particle Swarm Optimization (PSO) and an Artificial Fish Swarm (AFS), on the high school timetabling problem. In order to demonstrate their efficiency and performance, experiments with real-world input data have been performed. Both algorithms proposed manage to create feasible and efficient high school timetables, thus fulfilling adequately the timetabling needs of the respective high schools. Computational results demonstrate that both algorithms manage to reach efficient solutions, most of the times better than existing approaches applied to the same school timetabling input instances using the same evaluation criteria.

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“…19 A meta-heuristic is often developed in the context of a particular problem (or particular class of problem) and its performance outside of this context can therefore be variable. 20 Applications of meta-heuristics to course timetabling include: Barham and Westwood (1978);Tripathy (1980);Abramson (1991);Hertz (1991);Costa (1994);Alvarez-Valdes et al (1996);Wright (1996); Abramson et al (1999); Dimopoulou and Miliotis (2001); Mirrazavi et al (2003); Aladag et al (2009);Beligiannis et al (2009);De Causmaecker et al (2009);Moura and Scaraficci (2010);Zhang et al (2010); Al-Betar and Khader (2012); Burke et al (2012b);Geiger (2012); Pais and Amaral (2012);da Fonseca et al (2014); Lewis and Thompson (2015). 21 Applications of meta-heuristics to examinations timetabling include: Johnson (1990); Thompson and Dowsland (1998);Dimopoulou and Miliotis (2001); White et al (2004); Abdullah et al (2007); Burke et al (2010a); Özcan et al (2010); Turabieh and Abdullah (2011);Al-Betar et al (2014).…”

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confidence: 99%

Operational Research in education

Johnes

2015

European Journal of Operational Research

120

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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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A genetic algorithm approach to school timetabling

Cited by 40 publications

References 36 publications

Tabu Search Techniques for Polish Higher Education Timetabling Problems

Tabu Search Techniques for Polish Higher Education Timetabling Problems

A Comparative Study of Modern Heuristics on the School Timetabling Problem

Operational Research in education

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