Abstract
Traditional heuristic approaches to the Examination Timetabling
Problem normally utilize a stochastic method during Optimization for the
selection of the next examination to be considered for timetabling within
the neighbourhood search process. This paper presents a technique whereby
the stochastic method has been augmented with information from a weighted
list gathered during the initial adaptive construction phase, with the purpose
of intelligently directing examination selection. In addition, a Reinforcement
Learning technique has been adapted to identify the most effective portions
of the weighted list in terms of facilitating the greatest potential for overall
solution improvement. The technique is tested against the 2007 International
Timetabling Competition datasets with solutions generated within a
time frame specified by the competition organizers. The results generated are
better than those of the competition winner in seven of the twelve examinations,
while being competitive for the remaining five examinations. This paper
also shows experimentally how using reinforcement learning has improved
upon our previous technique.
Original language | English |
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Title of host publication | 10th International Conference of the Practice and Theory of Automated Timetabling |
Publisher | PATAT 2014 |
Pages | 218-232 |
Number of pages | 15 |
ISBN (Print) | 9730992998400 |
Publication status | Published - 26 Aug 2014 |
Event | 10th International Conference of the Practice and Theory of Automated Timetabling : PATAT 2014 - York, United Kingdom Duration: 26 Aug 2014 → 29 Aug 2014 http://www.patatconference.org/patat2014/index.html |
Conference
Conference | 10th International Conference of the Practice and Theory of Automated Timetabling |
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Country/Territory | United Kingdom |
City | York |
Period | 26/08/2014 → 29/08/2014 |
Internet address |