A Computational Intelligence Approach to Railway Track Intervention Planning

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Bartram:2008:ECP,
  author =       "Derek Bartram and Michael Burrow and Xin Yao",
  title =        "A Computational Intelligence Approach to Railway Track
                 Intervention Planning",
  booktitle =    "Evolutionary Computation in Practice",
  publisher =    "Springer",
  year =         "2008",
  editor =       "Tina Yu and David Davis and Cem Baydar and 
                 Rajkumar Roy",
  volume =       "88",
  series =       "Studies in Computational Intelligence",
  chapter =      "8",
  pages =        "163--198",
  keywords =     "genetic algorithms, genetic programming, k-means,
                 RPCL, learning",
  isbn13 =       "978-3-540-75770-2",
  DOI =          "doi:10.1007/978-3-540-75771-9_8",
  abstract =     "Railway track intervention planning is the process of
                 specifying the location and time of required
                 maintenance and renewal activities. To facilitate the
                 process, decision support tools have been developed and
                 typically use an expert system built with rules
                 specified by track maintenance engineers. However, due
                 to the complex interrelated nature of component
                 deterioration, it is problematic for an engineer to
                 consider all combinations of possible deterioration
                 mechanisms using a rule based approach. To address this
                 issue, this chapter describes an approach to the
                 intervention planning using a variety of computational
                 intelligence techniques. The proposed system learns
                 rules for maintenance planning from historical data and
                 incorporates future data to update the rules as they
                 become available thus the performance of the system
                 improves over time. To determine the failure type,
                 historical deterioration patterns of sections of track
                 are first analysed. A Rival Penalised Competitive
                 Learning algorithm is then used to determine possible
                 failure types. We have devised a generalised two stage
                 evolutionary algorithm to produce curve functions for
                 this purpose. The approach is illustrated using an
                 example with real data which demonstrates that the
                 proposed methodology is suitable and effective for the
                 task in hand.",
  notes =        "Part of \cite{TinaYu:2008:book}

                 Scheduling, railroad, maintenance, planning. Missing
                 data. Missing values.

                 p181 function set + - * / sin cos tan power",
}

Genetic Programming entries for Derek Bartram Michael Burrow Xin Yao

Citations