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Technical diagnostic of a fleet of vehicles using rough set theory

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  • Sawicki, Piotr
  • Zak, Jacek
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    Abstract

    The paper presents a process of technical diagnostic applied to a fleet of vehicles utilized in the delivery system of express mail. It is focused on evaluation of diagnostic capacity of particular characteristics, reduction of a set of initially selected characteristics to a minimal and satisfactory subset, recognition of a technical condition of vehicles resulting in their condition-based classification. In addition, the decision rules facilitating technical diagnostic and management of a fleet of vehicles are generated and utilized. N-fold cross validation is applied to estimate the efficiency of the decision rules. The rough set theory is applied to support the diagnostic process of vehicles. Classical rough set (CRS) theory is compared with the dominance-based rough set (DRS) approach. The results of computational experiments for both approaches are compared.

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    File URL: http://www.sciencedirect.com/science/article/B6VCT-4R41HYK-3/2/1e5396515ae5fd91b999dc8767cca50e
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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 193 (2009)
    Issue (Month): 3 (March)
    Pages: 891-903

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    Handle: RePEc:eee:ejores:v:193:y:2009:i:3:p:891-903

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Transportation Data mining Rough sets;

    References

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    1. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    2. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 2001. "Rough sets theory for multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 129(1), pages 1-47, February.
    3. Azibi, R. & Vanderpooten, D., 2002. "Construction of rule-based assignment models," European Journal of Operational Research, Elsevier, vol. 138(2), pages 274-293, April.
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