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

• Published in: Annals of operations research Vol. 239; no. 1; pp. 135 - 151
• Main Authors: Kheiri, Ahmed, Özcan, Ender, Parkes, Andrew J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2016; Springer; Springer Nature B.V
• Subjects: Acceptance; Algorithms; Business and Management; Combinatorics; Computation; Computer science; Educational aspects; Genetic algorithms; Heuristic; High schools; Integer programming; Management; Mathematical models; Methods; Neural networks; Operations research; Operations Research/Decision Theory; Scheduling; Scheduling (Management); Secondary schools; Solvers; Stochastic processes; Stochasticity; Strategy; Studies; Theory of Computation; Timetables; Timetabling; Scheduling; Stochastic local search; Restart; Hyper-heuristic
• ISSN: 0254-5330; 1572-9338; 1572-9338
• DOI: 10.1007/s10479-014-1660-0

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.