A stochastic local search algorithm with adaptive acceptance for high-school timetabling

Ahmed Kheiri (), Ender Özcan () and Andrew J. Parkes ()
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Ahmed Kheiri: University of Nottingham
Ender Özcan: University of Nottingham
Andrew J. Parkes: University of Nottingham

Annals of Operations Research, 2016, vol. 239, issue 1, No 8, 135-151

Abstract: Abstract Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers might fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective ‘heuristic to choose heuristics’ to solve such computationally hard problems. This paper describes the approach of the team hyper-heuristic search strategies and timetabling (HySST) to high school timetabling which competed in all three rounds of the third international timetabling competition. HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3. It achieved this by using a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism.

Keywords: Timetabling; Stochastic local search; Hyper-heuristic; Restart; Scheduling (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10479-014-1660-0

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