Exact/Heuristic Hybrids Using rVNS and Hyperheuristics for Workforce Scheduling

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

“…This removes bad heuristics early on and saves CPU time that would have been used trying them, but suffers from the potential limitation that it does not allow the reintroduction of these heuristics later in the search. The work of (Remde et al, 2007) shows that a low level heuristic's effectiveness is highly variable during search with some low level heuristics which are ineffective at the start of search proving highly effective at the end.…”

“…In some cases low level heuristics are parameterised, or composed by "multiplying" together components (Remde et al, 2007) (Chakhlevitch and Cowling, 2005), which can give rise to hundreds or even thousands of heuristics (Cowling and Chakhlevitch, 2003). In such a case, deciding in reasonable time which heuristics to use may be difficult.…”

“…However, there is evidence that having such a rich selection of low level heuristics may yield better results for complex problems in the long run, although it is difficult to know in advance which low level heuristics will prove effective (Chakhlevitch and Cowling, 2005). (Remde et al, 2007) studied the low level heuristics used by a greedy hyperheuristic HyperGreedy (which picks the best performing low level heuristic at each iteration) and found that a quarter of the low level heuristics were never used and half of the low level heuristics were effective less than one percent of the time. However, the low level heuristics which were effective varied from problem instance to problem instance, and it was difficult to predict which low level heuristics would prove effective.…”

“…In this paper we investigate hyperheuristic approaches that attempt to learn which low level heuristics will perform poorly and ignore them to produce solutions of a quality similar to those produced using the full set of low level heuristics, in a fraction of the CPU time. 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).…”

“…We use the problem and the hyperheuristic framework of (Remde et al, 2007) as a test bed for our investigation in this paper. The complexity of this problem means that quality evaluation of a perturbed solution takes a significant amount of CPU time, whereas limited amounts of CPU time are available to find good solutions in practice.…”