BRADFORD SCHOLARS

    • Sign in
    View Item 
    •   Bradford Scholars
    • Engineering and Informatics
    • Engineering and Informatics Publications
    • View Item
    •   Bradford Scholars
    • Engineering and Informatics
    • Engineering and Informatics Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Bradford ScholarsCommunitiesAuthorsTitlesSubjectsPublication DateThis CollectionAuthorsTitlesSubjectsPublication Date

    My Account

    Sign in

    HELP

    Bradford Scholars FAQsCopyright Fact SheetPolicies Fact SheetDeposit Terms and ConditionsDigital Preservation Policy

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    View/Open
    dahal12.pdf (247.4Kb)
    Download
    Publication date
    2007
    Author
    Remde, Stephen M.
    Cowling, Peter I.
    Dahal, Keshav P.
    Colledge, N.J.
    Keyword
    Workforce scheduling
    Reduced Variable Neighbourhood Search (rVNS)
    Hyperheuristic
    Genetic algorithms
    Optimimisation
    Rights
    © 2008 Springer-Verlag. Reproduced in accordance with the publisher's self-archiving policy. Original publication is available at http://www.springerlink.com
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    In this paper we study a complex real-world workforce scheduling problem. We propose a method of splitting the problem into smaller parts and solving each part using exhaustive search. These smaller parts comprise a combination of choosing a method to select a task to be scheduled and a method to allocate resources, including time, to the selected task. We use reduced Variable Neighbourhood Search (rVNS) and hyperheuristic approaches to decide which sub problems to tackle. The resulting methods are compared to local search and Genetic Algorithm approaches. Parallelisation is used to perform nearly one CPU-year of experiments. The results show that the new methods can produce results fitter than the Genetic Algorithm in less time and that they are far superior to any of their component techniques. The method used to split up the problem is generalisable and could be applied to a wide range of optimisation problems.
    URI
    http://hdl.handle.net/10454/2510
    Version
    Accepted Manuscript
    Citation
    Remde, S. M., Cowling, P. I., Dahal, K. P. and Colledge, N. J. (2007) Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling. In: Evolutionary computation in combinatorial optimization. Proceedings of the 7th European Conference (EvoCOP 2007) Valencia, Spain, April 11-13, 2007. pp 188-197.
    Link to publisher’s version
    http://www.springerlink.com/
    Type
    Conference paper
    Collections
    Engineering and Informatics Publications

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.