Applying the threshold accepting metaheuristic to curriculum based course timetabling

Abstract

The article presents a study of local search algorithms for timetabling problems, with the particular goal of providing a contribution to competition track 3 of the International Timetabling Competition 2007 (ITC 2007). In this track, a formulation of a curriculum based course timetabling has been published, and novel benchmark instances have been presented that allow the comparison of optimization approaches.Our heuristic local search procedure is based on the principles of Threshold Accepting, overcoming local optima by a deterministic acceptance of inferior solutions throughout the search runs. A stochastic neighborhood is proposed and implemented, randomly removing and reassigning events from the current solution.The overall concept has been incrementally obtained from a series of experiments, which we describe in each (sub)section of the paper. In conclusions, we successfully derived a potential candidate solution approach for the finals of track 3 of the ITC 2007, held in August 2008 in Montréal, Canada.

Developing compact course timetables with optimized student flows

“…Recent examples of metaheuristic approaches applied to UCTPs can be found in De Causmaecker et al [16], Lü and Hao [17], Aladag et al [18], Zhang et al [19] and Geiger [20]. A hyperheuristic is a framework in which an upper-level metaheuristic selects the most appropriate heuristic out of a set of lower-level heuristics to solve a particular optimization problem (Petrovic and Burke,[8]).…”

“…These constraints work as follows: if two consecutive lectures l and m, which are followed by series s, are planned in rooms c and d respectively, then (20) and (21) reduce to:…”

Lower bounds for the ITC-2007 curriculum-based course timetabling problem

“…Soft constraints are used to define the objective function to be minimized. Examples of recent studies on university timetabling can be found in [1,2,4,5,6,10,12,14,16,18].…”

“…The ITC-2007 has led to the development of many heuristic solution approaches. The five top performing approaches (finalists) of the ITC-2007 include a hybrid approach [16], a tabu search [14], a general CSP solver [2,17], a threshold accepting approach [10], and a repair-based heuristic approach [6]. These heuristic approaches produce only upper bounds to this minimization problem.…”

Operational Research in education

“…19 A meta-heuristic is often developed in the context of a particular problem (or particular class of problem) and its performance outside of this context can therefore be variable. 20 Applications of meta-heuristics to course timetabling include: Barham and Westwood (1978);Tripathy (1980);Abramson (1991);Hertz (1991);Costa (1994);Alvarez-Valdes et al (1996);Wright (1996); Abramson et al (1999); Dimopoulou and Miliotis (2001); Mirrazavi et al (2003); Aladag et al (2009);Beligiannis et al (2009);De Causmaecker et al (2009);Moura and Scaraficci (2010);Zhang et al (2010); Al-Betar and Khader (2012); Burke et al (2012b);Geiger (2012); Pais and Amaral (2012);da Fonseca et al (2014); Lewis and Thompson (2015). 21 Applications of meta-heuristics to examinations timetabling include: Johnson (1990); Thompson and Dowsland (1998);Dimopoulou and Miliotis (2001); White et al (2004); Abdullah et al (2007); Burke et al (2010a); Özcan et al (2010); Turabieh and Abdullah (2011);Al-Betar et al (2014).…”