A hybrid fuzzy adaptive sampling - Run rules for Shewhart control charts

  • Authors:

  • M.H. Fazel Zarandi;A. Alaeddini;I. B. Turksen

  • Affiliations:

  • Department of Industrial Engineering, Amirkabir University of Technology, P.O. Box 15875-3144, Tehran, Iran;Department of Industrial Engineering, Iran University of Science and Technology, P.O. Box 16846-13114, Tehran, Iran;Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada M5S2H8 and Department of Industrial Engineering, TOBB University of Economics and Technology, S ...

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

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Abstract

In crisp run control rules, usually it is stated that a process moves very sharply from in-control condition to out-of-control act. This causes an increase in both false-alarm rate and control chart sensitivity. Moreover, the classical run control rules are not implemented on an intelligent sampling strategy that changes control charts' parameters to reduce error probability when the process appears to have a shift in parameter values. This paper presents a new hybrid method based on a combination of fuzzified sensitivity criteria and fuzzy adaptive sampling rules, which make the control charts more sensitive and proactive while keeping false alarms rate acceptably low. The procedure is based on a simple strategy that includes varying control chart parameters (sample size and sample interval) based on current fuzzified state of the process and makes inference about the state of process based on fuzzified run rules. Furthermore, in this paper, the performance of the proposed method is examined and compared with both conventional run rules and adaptive sampling schemes.