A Tabu-Search Hyperheuristic for Timetabling and Rostering

Burke, Edmund K.; Kendall, Graham; Soubeiga, Eric

doi:10.1023/b:heur.0000012446.94732.b6

Cited by 431 publications

(307 citation statements)

References 21 publications

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“…A tabu search based hyper-heuristic has also been developed which was effective on both a nurse rostering problem and a university course timetabling problem which demonstrated the level of generality of the method (Burke, Kendall and Soubeiga 2003). In this approach, the hyper-heuristic dynamically ranks a set of heuristics according to their performance in the search history.…”

Section: Hyper-heuristics: An Overviewmentioning

confidence: 99%

“…The simulated annealing hyper-heuristic we propose in this paper builds upon the methodologies presented in (Bai and Kendall 2005) and (Burke, Kendall and Soubeiga 2003).…”

Section: Hyper-heuristics: An Overviewmentioning

confidence: 99%

“…Hyper-heuristics have recently received significant research attention , Burke, Kendall and Soubeiga 2003, Ross 2005 in order to address some of these issues. In (Venkatraman and Yen 2005), a generic two-stage evolutionary algorithm framework was proposed for constrained optimisation problems.…”

Section: Introductionmentioning

confidence: 99%

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A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

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Abstract. One of the main motivations for investigating hyper-heuristic methodologies is to provide a more general search framework than is currently available. Most of the current search techniques represent approaches that are largely adapted for specific search problems (and, in some cases, even specific problem instances). There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are more generally applicable to more problems. One of our motivating goals is to underpin the development of more flexible search methodologies that can be easily and automatically employed on a broader range of problems than is currently possible. Almost all the heuristics that have appeared in the literature have been designed and selected by humans. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark data sets drawn from three very different and difficult (NP-hard) problems: nurse rostering, university course timetabling and onedimensional bin packing. Experimental results show that the proposed hyper-heuristic is able to achieve significant performance improvements over a recently proposed tabu search hyper-heuristic without lowering the level of generality. We also show that our hyper-heuristic is capable of producing competitive results against bespoke meta-heuristics methods for these problems. In some cases, the simulated annealing hyper-heuristic has even obtained considerable improvements over some of the current best problem-specific meta-heuristic approaches. The contribution of this paper is to present a method which can be readily (and automatically) applied to very different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.

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Section: Hyper-heuristics: An Overviewmentioning

confidence: 99%

“…The simulated annealing hyper-heuristic we propose in this paper builds upon the methodologies presented in (Bai and Kendall 2005) and (Burke, Kendall and Soubeiga 2003).…”

Section: Hyper-heuristics: An Overviewmentioning

confidence: 99%

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A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

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“…Since personnel scheduling problems are general NP-hard combinatorial problems (Garey and Johnson, 1979) which are unlikely to be solved optimally in polynomial time, various methods such as local search-based heuristics (Li and Kwan, 2005), knowledge based systems (Scott and Simpson, 1998) and hyper-heuristics (Burke, Kendall, and Soubeiga, 2003) have been studied. Over the last few years, metaheuristics have attracted the most attention.…”

Section: Introductionmentioning

confidence: 99%

Improved Squeaky Wheel Optimisation for Driver Scheduling

Aickelin

Burke

2006

Parallel Problem Solving From Nature - PPSN IX

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Abstract. This paper presents a technique called Improved Squeaky Wheel Optimisation (ISWO) for driver scheduling problems. It improves the original Squeaky Wheel Optimisation's (SWO) effectiveness and execution speed by incorporating two additional steps of Selection and Mutation which implement evolution within a single solution. In the ISWO, a cycle of Analysis-SelectionMutation-Prioritization-Construction continues until stopping conditions are reached. The Analysis step first computes the fitness of a current solution to identify troublesome components. The Selection step then discards these troublesome components probabilistically by using the fitness measure, and the Mutation step follows to further discard a small number of components at random. After the above steps, an input solution becomes partial and thus the resulting partial solution needs to be repaired. The repair is carried out by using the Prioritization step to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, the optimisation in the ISWO is achieved by solution disruption, iterative improvement and an iterative constructive repair process performed. Encouraging experimental results are reported.

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“…Hyper-heuristics are problem independent and have been successfully applied to various optimisation and scheduling problems [19,20,21,22]. For example, a genetic algorithm hyper-heuristic [20] used an allele in a chromosome to represent each LLH.…”

Section: Hyper-heuristicmentioning

confidence: 99%

Channel assignment optimisation using a hyper-heuristic

Kendall

Mohamad

IEEE Conference on Cybernetics and Intelligent Systems, 2004.

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-The channel assignment problem is a real world problem from the mobile communications industry. The primary objective is to find the minimum frequency bandwidth given different traffic demand distribution within the mobile network. Besides fulfilling the channel demand requirement, we must also consider the minimum channel reuse distance in order to avoid the effect of call interference within the same cell or adjacent cells. Previous approaches have used graph-colouring algorithms, heuristic search, neural networks, simulated annealing and genetic algorithms (GA). In this paper, we propose a hyperheuristic methodology and compare our approach against channel assignment problems from the literature.

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A Tabu-Search Hyperheuristic for Timetabling and Rostering

Cited by 431 publications

References 21 publications

A simulated annealing hyper-heuristic methodology for flexible decision support

A simulated annealing hyper-heuristic methodology for flexible decision support

Improved Squeaky Wheel Optimisation for Driver Scheduling

Channel assignment optimisation using a hyper-heuristic

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