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

Corr, Patrick H.; McCollum, Barry; McGreevy, M. A. J.; McMullan, Paul

doi:10.1007/11844297_40

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…In [23] a neural network framework for timetabling was presented. The system dynamically changes the ordering criteria as the solution is constructed through the use of a neural network which updates the difficulty ranking of each of the remaining exams based on the current state of the solution.…”

Section: Graph-based Methodsmentioning

confidence: 99%

Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework

2009

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This paper introduces a Grammar-based Genetic Programming Hyper-Heuristic framework (GPHH) for evolving constructive heuristics for timetabling. In this application GP is used as an online learning method which evolves heuristics while solving the problem. In other words, the system keeps on evolving heuristics for a problem instance until a good solution is found. The framework is tested on some of the most widely used benchmarks in the field of exam timetabling and compared with the best state-of-the-art approaches. Results show that the framework is very competitive with other constructive techniques, and did outperform other hyper-heuristic frameworks on many occasions.

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Section: Graph-based Methodsmentioning

confidence: 99%

Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework

2009

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“…In addition to the hyper-heuristics based on constructive low level heuristics (Kendall and Hussin 2005;Burke et al 2007b), tabu search (Gaspero and Schaerf 2001), very large neighbourhood search (Abdullah et al 2007), simulated annealing (Merlot et al 2003), multi-stage approaches utilising case based reasoning (Petrovic et al 2007), an iterative greedy algorithm (Caramia et al 2001(Caramia et al , 2008 and great deluge (Müller 2009), fuzzy reasoning (Petrovic and Patel 2005;Asmuni et al 2005), neural network (Corr et al 2006) and ant colony optimisation (Dowsland and Thompson 2005;Eley 2006) based approaches and hybrid methods (Azimi 2005;Gogos et al 2010) are some of the other techniques used to solve different types of examination timetabling problems.…”

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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“…Mainly in the field of Operations Research and Artificial Intelligence many interesting proposals have been presented, to solve timetabling problems in sports (Easton et al 2004;Trick 2001), transportations (bus, railways, planes) (Isaai and Singh 2001;Caprara et al 2001;Kendall and Mohd Hussin 2003;Qi et al 2004), schools (Abramson et al 1999;Colorni et al 1998;Ribeiro Filho and Lorena 2001;Hansen and Vidal 1995;Schaerf 1999) and universities (Awad and Chinneck 1998;Burke et al 2006;Burke and Newall 2003;Caramia et al 2001;Casey and Thompson 2003;Carter et al 1994;Corr et al 2006;Desroches et al 1978;Dowsland and Thompson 2005;Erben 2001;Di Gaspero 2000;Di Gaspero and Schaerf 2001;Kendall and Mohd Hussin 2003;Merlot et al 2003;Paquete and Fonseca 2001;Petrovic and Bykov 2003;Qu et al 2006;Schimmelpfeng and Helber 2007;Thompson and Dowsland 1996; Thompson and Dowsland 1998;White and Xie 2001;Yang and Petrovic 2005).…”

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confidence: 98%

Managing the tabu list length using a fuzzy inference system: an application to examination timetabling

Pais

Amaral

2011

Ann Oper Res

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In this paper, we present an application of Tabu Search (TS) to the examination timetabling problem. One of the drawbacks of this meta-heuristic is related to the need of tuning some parameter (like tabu tenure) whose value affects the performance of the algorithm. The importance of developing an automatic procedure is clear considering that most of the users of timetabling software, like academic staff, do not have the expertise to conduct such tuning. The goal of this paper is to present a method to automatically manage the memory in the TS using a Decision Expert System. More precisely a Fuzzy Inference Rule Based System (FIRBS) is implemented to handle the tabu tenure based on two concepts, "Frequency" and "Inactivity". These concepts are related respectively with the number of times a move is introduced in the tabu list and the last time (in number of iterations) the move was attempted and prevented by the tabu status. Computational results show that the implemented FIRBS handles well the tuning of the tabu status duration improving, as well, the performance of Tabu Search.

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

Cited by 15 publications

References 18 publications

Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework

Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework

Monte Carlo hyper-heuristics for examination timetabling

Managing the tabu list length using a fuzzy inference system: an application to examination timetabling

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