Article

Benchmarking curriculum-based course timetabling: formulations, data formats, instances, validation, visualization, and results.

Annals of Operations Research (Impact Factor: 1.03). 01/2012; 194:59-70. DOI:10.1007/s10479-010-0707-0
Source: DBLP

ABSTRACT We propose a set of formulations for the Curriculum-Based Course Timetabling problem, with the aim of “capturing” many real-world
formulations, and thus encouraging researchers to “reduce” their specific problems to one of them, gaining the opportunity
to compare and assess their results. This work is accompanied by a web application that maintains all the necessary infrastructures for benchmarking: validators, data formats, instances, reference scores, lower bounds, solutions, and visualizers. All instances
proposed here are based on real data from various universities and they represent a variety of possible situations.

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    ABSTRACT: Timetabling is the task of assigning sets of events to periods of time, taking into account resource-constraints and preferences among assignments. It is a well-studied field of research and is generally recog-nized to be a hard problem, both from the perspective of encoding it as from a computational point of view. In recent years, there has been increased interest in combining efficient search algorithms with modelling languages capable of high-level rep-resentations of real-world domains. Research into such systems is con-ducted, a.o., in the fields of Constraint Programming and Knowledge Representation. In this paper, we investigate the use of the modelling language of first-order logic extended with constructs such as aggregates, types, definitions and arithmetic, and the idp system, which implements model generation for this language, to the timetabling problem. We show the feasibility of the approach and argue that there are impor-tant advantages both from the modelling point of view, leading to nat-ural representation of the problem, and from the solving point of view, taking advantage of efficient automated search techniques like SAT and constraint propagation.
    01/2011;
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    ABSTRACT: The curriculum based course timetabling problem is a well-researched domain for which there are known benchmark data sets. Various techniques have been applied to these benchmarks in order to identify a methodology that produces the best quality timetable for one or more of the benchmark problems. The study presented in this paper takes a different approach and aims at developing a system, namely, an evolutionary algorithm (EA) hyper-heuristic, that generalizes over a set of problems rather than only producing a feasible timetable for one or more of the problems. The results of a first attempt at implementing an EA hyper-heuristic to solve the curriculum based university course timetabling problem is presented. The EA hyper-heuristic searches a heuristic space of combinations of low-level construction heuristics for feasibility, instead of a solution space. The optimal heuristic combination evolved is used to construct a solution to the timetabling problem. The EA hyper-heuristic was tested on the benchmark set of curriculum based course timetabling problems used for the second international timetabling competition. The system evolved feasible solutions for all 14 problems. The study also revealed areas for further improvement.
    Second World Congress on Nature & Biologically Inspired Computing, NaBIC 2010, 15-17 December 2010, Kitakyushu, Japan; 01/2010
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    Annals of Operations Research 01/2011; · 1.03 Impact Factor

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Nov 22, 2012