Hyper-heuristics applied to class and exam timetabling problems

European Journal of Operational Research

Eckersley²,

McCollum

et al. 2010

118

Abstract. In this paper, we investigate variable neighbourhood search (VNS) approaches for the university examination timetabling problem. In addition to a basic VNS method, we introduce variants of the technique with different initialisation methods including a biased VNS and its hybridisation with a Genetic Algorithm. A number of different neighbourhood structures are analysed. It is demonstrated that the proposed technique is able to produce high quality solutions across a wide range of benchmark problem instances. In particular, we demonstrate that the Genetic Algorithm, which intelligently selects approporiate neighbourhoods to use within the biased VNS produces the best known results in the literature, in terms of solution quality, on some of the the benchmark instances, although it requires relatively large amount of computational time. Possible extensions to this overall approach are also discussed.

Section: Examination Timetabling Techniquesmentioning

confidence: 99%

Section: The Hybrid Vns Approachmentioning

confidence: 99%

Hybrid variable neighbourhood approaches to university exam timetabling

European Journal of Operational Research

Eckersley²,

McCollum

et al. 2010

118

“…[15,28,32]) or on using constructive heuristics [6,11,17,21,48,51] as the low level heuristics. In [15], Tabu Search was employed as the high level heuristic upon a set of moving strategies on both the nurse rostering problems and course timetabling.…”

Section: Hyper-heuristicsmentioning

confidence: 99%

“…In [21], graph colouring heuristics were selected by using Case Based Reasoning to order the events upon problem solving situations. A Genetic Algorithm was also developed in [48] to evolve event picking and slot picking heuristics to construct timetables for both class and exam benchmark timetabling problems. Issues of representation and fitness functions were also addressed.…”

Section: Hyper-heuristicsmentioning

confidence: 99%

Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems

Journal of the Operational Research Society

2009

Abstract. A significant body of recent literature has explored various research directions in hyperheuristics (which can be thought as heuristics to choose heuristics). In this paper, we extend our previous work to construct a unified graph based hyper-heuristic (GHH) framework, under which a number of local search based algorithms as the high level heuristics are studied to search upon sequences of low level graph colouring heuristics. To gain an in-depth understanding on this new framework, we address some fundamental issues concerning neighbourhood structures and characteristics of the two search spaces (namely the search spaces of the heuristics and the actual solutions). Furthermore, we investigate efficient hybridisations in GHH with local search methods and address issues concerning the exploration of the high level search and the exploitation ability of the local search. These, to our knowledge, represent entirely novel directions in hyper-heuristics. The efficient hybrid GHH obtained competitive results compared with the best published results for both benchmark course and exam timetabling problems, demonstrating its efficiency and generality across different problem domains. Possible extensions upon this simple yet general GHH framework are also discussed.

“…The approach produced feasible examination timetables with soft constraints within the range of other search methods employed for this purpose, and outperformed previous hyper-heuristics on a number of the tested instances. Ross et al (2004) and Ross and Marı´n-Blázquez (2005) apply a messy genetic algorithm (Goldberg et al, 1990) hyper-heuristic based on graph colouring heuristics to both class and exam timetabling problems. The idea is to learn associations between problem states and adequate heuristics for timetabling.…”

Section: Approaches Based On Constructive Low-level Heuristicsmentioning

confidence: 99%

Hyper-heuristics: a survey of the state of the art

Journal of the Operational Research Society

Gendreau

Hyde

et al. 2013

985

550

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.