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Technical diagnostic of a fleet of vehicles using rough set theory

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  • Sawicki, Piotr
  • Zak, Jacek

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.

Suggested Citation

  • Sawicki, Piotr & Zak, Jacek, 2009. "Technical diagnostic of a fleet of vehicles using rough set theory," European Journal of Operational Research, Elsevier, vol. 193(3), pages 891-903, March.
    • Handle: RePEc:eee:ejores:v:193:y:2009:i:3:p:891-903
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    References listed on IDEAS

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    1. 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.
    2. R. Slowinski & C. Zopounidis, 1995. "Application of the Rough Set Approach to Evaluation of Bankruptcy Risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(1), pages 27-41, March.
    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.
    4. 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.
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    Cited by:

    1. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    2. Chunguang Bai & Behnam Fahimnia & Joseph Sarkis, 2017. "Sustainable transport fleet appraisal using a hybrid multi-objective decision making approach," Annals of Operations Research, Springer, vol. 250(2), pages 309-340, March.
    3. Shyng, Jhieh-Yu & Shieh, How-Ming & Tzeng, Gwo-Hshiung & Hsieh, Shu-Huei, 2010. "Using FSBT technique with Rough Set Theory for personal investment portfolio analysis," European Journal of Operational Research, Elsevier, vol. 201(2), pages 601-607, March.
    4. Matthew G. Karlaftis, 2011. "Modeling transit vehicle repair duration and active service time," Transportation Planning and Technology, Taylor & Francis Journals, vol. 34(5), pages 433-442, April.

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