Subtractive clustering based modeling of job sequencing with parametric search

  • Authors:

  • K. Demirli;S. X. Cheng;P. Muthukumaran

  • Affiliations:

  • Department of Mechanical and Industrial Engineering, Fuzzy Systems Research Laboratory, Concordia University, 1455 de Maisonneuve Blvd. W., Montreal, Que., Canada H3G 1M8;Department of Mechanical and Industrial Engineering, Fuzzy Systems Research Laboratory, Concordia University, 1455 de Maisonneuve Blvd. W., Montreal, Que., Canada H3G 1M8;Department of Mechanical and Industrial Engineering, Fuzzy Systems Research Laboratory, Concordia University, 1455 de Maisonneuve Blvd. W., Montreal, Que., Canada H3G 1M8

  • Venue:
  • Fuzzy Sets and Systems - Data analysis
  • Year:
  • 2003

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Abstract

In this paper, an extended subtractive clustering based fuzzy system identification method and the Sugeno type reasoning mechanism are used for modeling job sequencing problems. This approach can be used to build a fuzzy model of the sequencing system from an existing sequence (output data) and possible job attributes (input data). The single machine weighted flowtime problem is used as an example to demonstrate the proposed methodology. The effects of data scarcity on the modeling performance is studied by using three data sets with Varying degrees of available data. Furthermore, a parametric search on various clustering parameters is performed to identify the best model. As a result of parametric search, ranges of clustering parameters that provide best models are also identified.