Directed Selection using Reinforcement Learning for the Examination Timetabling Problem

Ryan Hamilton-Bryce, Paul McMullan, Barry McCollum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish
Title of host publication10th International Conference of the Practice and Theory of Automated Timetabling
PublisherPATAT 2014
Pages218-232
Number of pages15
ISBN (Print)9730992998400
Publication statusPublished - 26 Aug 2014
Event10th International Conference of the Practice and Theory of Automated Timetabling : PATAT 2014 - York, United Kingdom
Duration: 26 Aug 201429 Aug 2014
http://www.patatconference.org/patat2014/index.html

Conference

Conference10th International Conference of the Practice and Theory of Automated Timetabling
Country/TerritoryUnited Kingdom
CityYork
Period26/08/201429/08/2014
Internet address

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