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Decision support system for scheduling a Flexible Flow System: Incorporation of feature construction

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  • Selwyn Piramuthu
  • Narayan Raman
  • Michael Shaw

Abstract

We study the problem of scheduling a Flexible Flow System (FFS) while utilizing machine learning techniques. Specifically, the effect of incorporating Feature Construction in an existing adaptive learning-based decision support system is investigated. The primary purpose of constructing features is to increase the efficiency of obtaining information from available data by using new features in addition to the initial set of features. There is a need to obtain information effectively while scheduling an FFS. The incorporation of feature construction in such a framework is thus beneficial, which is documented in our preliminary results. Copyright Kluwer Academic Publishers 1998

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  • Selwyn Piramuthu & Narayan Raman & Michael Shaw, 1998. "Decision support system for scheduling a Flexible Flow System: Incorporation of feature construction," Annals of Operations Research, Springer, vol. 78(0), pages 219-234, January.
    • Handle: RePEc:spr:annopr:v:78:y:1998:i:0:p:219-234:10.1023/a:1018902200919
    DOI: 10.1023/A:1018902200919
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    Cited by:

    1. Wei Zhou & Selwyn Piramuthu, 2017. "Identification shrinkage in inventory management: an RFID-based solution," Annals of Operations Research, Springer, vol. 258(2), pages 285-300, November.
    2. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
    3. Weiwei Chen & Jie Song & Leyuan Shi & Liang Pi & Peter Sun, 2013. "Data mining-based dispatching system for solving the local pickup and delivery problem," Annals of Operations Research, Springer, vol. 203(1), pages 351-370, March.

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