Prioritizing Pipe Replacement: From Multiobjective Genetic Algorithms to Operational Decision Support

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Giustolisi:2009:JWRPM,
  author =       "Orazio Giustolisi and Luigi Berardi",
  title =        "Prioritizing Pipe Replacement: From Multiobjective
                 Genetic Algorithms to Operational Decision Support",
  journal =      "Journal of Water Resources Planning and Management",
  year =         "2009",
  volume =       "135",
  number =       "6",
  pages =        "484--492",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, Decision
                 support systems, Water distribution systems, Water
                 pipelines, Multiple objective analysis, Replacement,
                 Rehabilitation",
  ISSN =         "0733-9496",
  DOI =          "doi:10.1061/(ASCE)0733-9496(2009)135:6(484)",
  size =         "9 pages",
  abstract =     "Deterioration of water distribution systems and the
                 optimal allocation of limited funds for their
                 rehabilitation represent crucial challenges for water
                 utility managers. Decision makers should be provided
                 with a set of informed solutions to select the best
                 rehabilitation plan with regard to available resources
                 and management strategies. In a risk-based scenario,
                 such an approach should result in an element-wise
                 prioritisation scheme based on individual pipe
                 rehabilitation/replacement effectiveness. This
                 manuscript describes a framework for devising a
                 short-term decision support tool for pipe replacement.
                 The approach allows for the introduction of economic,
                 technical, and management rationales as separate
                 objectives to produce a pipe-wise prioritisation scheme
                 which is achieved by ranking pipes selected during a
                 multiobjective (MO) evolutionary optimisation of
                 replacement scenarios. Such a procedure helps overcome
                 the doubts in choosing among the solutions obtained by
                 MO evolutionary optimization due to the diverse sets of
                 pipes selected for replacement even when they are
                 economically comparable. The effectiveness of the
                 entire framework is demonstrated on a real U.K. water
                 distribution system.",
  notes =        "Publisher: ASCE, American Society of Civil
                 Engineers.

                 Papers using GP related results",
}

Genetic Programming entries for Orazio Giustolisi Luigi Berardi

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