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.
International audienceBuilding on previous work of the authors, this paper formally defines and reviews the first approach, referred to as navigation, towards a common understanding of search and decision-making strategies to identify the most preferred solution among the Pareto set for a multiobjective optimization problem. In navigation methods, the decision maker interactively learns about the problem, whereas the decision support system learns about the preferences of the decision maker. This work introduces a detailed view on navigation leading to the identification of integral components and features. A number of different existing navigation methods are reviewed and characterized. Finally, an overview of applications involving navigation is given, and promising future research direction are discussed
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