• DocumentCode
    2781153
  • Title

    Benchmarks for maintenance scheduling problems in power generation

  • Author

    Almakhlafi, Ahmad ; Knowles, Joshua

  • Author_Institution
    Sch. of Comput. Sci., Univ. of Manchester, Manchester, UK
  • fYear
    2012
  • fDate
    10-15 June 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    We present a test suite of 23 instances of a preventive maintenance scheduling problem from the power industry, which we also make available online. The formulation of the problem and the suite are derived from real-world data collected recently. A first study of the landscape characteristics of these problem instances based on three different types of adaptive walk reveals a generally rugged landscape, with little global fitness-distance correlation. Initial results from a simple evolutionary algorithm shows indifferent performance compared to adaptive walks, suggesting that intensive local search may be an important component of a successful optimizer for this problem.
  • Keywords
    electricity supply industry; evolutionary computation; power generation scheduling; preventive maintenance; search problems; adaptive walk; benchmarks; evolutionary algorithm; generally rugged landscape; global fitness-distance correlation; intensive local search; landscape characteristics; maintenance scheduling problems; power generation; power industry; preventive maintenance scheduling problem; problem instances; real-world data; Correlation; Generators; Job shop scheduling; Maintenance engineering; Optimization; Planning; Power generation; Maintenance scheduling; benchmarks; fitness landscape analysis; genetic algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2012 IEEE Congress on
  • Conference_Location
    Brisbane, QLD
  • Print_ISBN
    978-1-4673-1510-4
  • Electronic_ISBN
    978-1-4673-1508-1
  • Type

    conf

  • DOI
    10.1109/CEC.2012.6252988
  • Filename
    6252988