Local Search and Constraint Programming for a Real-World Examination Timetabling Problem

We investigate the examination timetabling problem in the context of Italian universities. The outcome is the definition of a general problem that can be applied to a large set of universities, but is quite different in many aspects from the classical versions proposed in the literature. We propose both a metaheuristic approach based on Simulated Annealing and a Constraint Programming model in MiniZinc. We compare the results of the metaheuristic approach (properly tuned) with the available MiniZinc back-ends on a large set of diverse real-world instances

A tabu based large neighbourhood search methodology for the capacitated examination timetabling problem

Neighbourhood search algorithms are often the most effective known approaches for solving partitioning problems. In this paper, we consider the capacitated examination timetabling problem as a partitioning problem and present an examination timetabling methodology that is based upon the large neighbourhood search algorithm that was originally developed by Ahuja and Orlin. It is based on searching a very large neighbourhood of solutions using graph theoretical algorithms implemented on a so-called improvement graph. In this paper, we present a tabu-based large neighbourhood search, in which the improvement moves are kept in a tabu list for a certain number of iterations. We have drawn upon Ahuja-Orlin's methodology incorporated with tabu lists and have developed an effective examination timetabling solution scheme which we evaluated on capacitated problem benchmark data sets from the literature. The capacitated problem includes the consideration of room capacities and, as such, represents an issue that is of particular importance in real-world situations. We compare our approach against other methodologies that have appeared in the literature over recent years. Our computational experiments indicate that the approach we describe produces the best known results on a number of these benchmark problems