A Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology

Abstract

Abstract. In this paper we introduce an examination timetabling problem from the MARA University of Technology (UiTM). UiTM is the largest university in Malaysia. It has 13 branch campuses and offers 144 programmes, delivered by 18 faculties. This dataset differs from the others reported in the literature due to weekend constraints that have to be observed. We present their examination timetabling problem with respect to its size, complexity and constraints. We analyse their real-world data, and produce solutions utilising a tabu-search-based hyper-heuristic. Since this is a new dataset, and no solutions have been published in the literature, we can only compare our results with an existing manual solution. We find that our solution is at least 80% better with respect to proximity cost. We also compare our approach against a benchmark dataset and show that our method is able to produce good quality results.

An empirical study of hyperheuristics for managing very large sets of low level heuristics

“…The fixed tabu tenure hyperheuristics (Tabu) perform poorly in comparison to the random tabu tenure hyperheuristics (rTabu), supporting the conclusions of (Rolland et al, 1996) (Kendall and Hussin, 2005a). The Tabu results are all well below the threshold given by the Sample points, indicating that these approaches perform significantly worse than random choice per second of CPU time.…”

“…A tabu mechanism is used in (Kendall and Hussin, 2005a) where poorly performing low level heuristics are made tabu for a fixed tabu tenure. A small number of low level heuristics (13) are used with short tabu tenures (1-4 iterations) and good results are obtained in a large amount of CPU time.…”

“…In addition to these fixed tenures, we try random tenures as used in (Kendall and Hussin, 2005a): rTabu Best x t=y is similar to Tabu Best x t=y, but with a random tenure between 0 and y each time a low level heuristic is made tabu.…”

“…This is particularly of interest since hyperheuristic methods have demonstrated their effectiveness at solving problems such as automated planograms (Bai and Kendall, 2005), examination scheduling (Burke et al, 2003), personnel scheduling (Cowling et al, 2003), workforce scheduling (Remde et al, 2007) and artificial intelligence in computer games (Nareyek, 2004). These applications have shown that hyperheuristics offer some of the solution quality we would associate with tailored methods, but that they are very flexible in dealing with different problem instances (and indeed different problems) and that they remain effective when the problem is changed in significant ways, without requiring substantial intervention from a human expert (Kendall and Hussin, 2005a).…”

An Ant Based Hyper-heuristic for the Travelling Tournament Problem

“…They show that individual heuristics are not able to find optimal solutions to any of the problems, but using a hyper-heuristic, to combine heuristics, they were able to find optimal solutions for 78% of the cases. Other hyper-heuristic examples can be found in [19], [20,21] (timetabling), [22] (determining shipper sizes), [23] (presentation scheduling), [24] (shelf space layout), [25,26] (channel assignment) and [27] (component placement).…”