An integer program and a hybrid genetic algorithm for the university timetabling problem Xuehao Feng, Yuna Lee & Ilkyeong Moon To cite this article: Xuehao Feng, Yuna Lee & Ilkyeong Moon (2017) An integer program and a hybrid genetic algorithm for the university timetabling problem, Optimization Methods and Software, 32:3, 625-649, DOI: 10.1080/10556788.2016.1233970 To link to this article: htt...

STTL is a language for specifying and evaluating timetabling problems, instances, and solutions. An interpreter for STTL is freely available on the Internet. After a brief overview of the language, this paper explores the issues involved in applying STTL to real-world problems,using high school timetabling as a case study.

We present the progress on the benchmarking project for high school timetabling that was introduced at PATAT 2008. In particular, we announce the High School Timetabling Archive XHSTT-2011 with 21 instances from 8 countries and an evaluator capable of checking the syntax of instances and evaluating the solutions.

This work presents a heuristic approach proposed by one of the finalists of the Third International Timetabling Competition (ITC2011). The KHE school timetabling engine is used to generate an initial solution and then Simulated Annealing (SA) and Iterated Local Search (ILS) perform local search around this solution.

This paper describes how the university timetabling problem is addressed in the Laurea course Ingegneria dell’Informazione (Information Engineering) for the University of Ferrara, Italy. The university timetabling problem is modelled as a Constraint Optimisation Problem and addressed with ECLPS, one of the leading Constraint Logic Programming languages.

Various researchers have been applying evolutionary algorithms (EAs) to timetabling problems in the last few years. In this article we swiftly overview EA approaches to timetabling. We pay particular attention to discussing the salient aspects of approaches used, note results so far, and consider the general prospects of this pursuit.

We present here a metaheuristic approach to the university course timetabling problem using a memetic algorithm with a very effective local search. The algorithm was tested on the International Timetabling Competition benchmark instances. Results are encouraging and show that evolutionary computation with the help of local search can compete with succesful algorithms on the problem.