Using data continualization and expansion to improve small data set learning accuracy for early flexible manufacturing system (FMS) scheduling

Der-Chiang Li, C. Wu, F. M. Chang

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Knowledge derived from limited data gathered in the early manufacturing stages is usually too fragile for a flexible manufacturing system (FMS). Unfortunately, production decisions have to be made quickly in a competitive environment. In a previous study, a strategy using continuous data and domain external expansion methods under a known data domain range was proposed to solve the so-called small data set learning problem in FMS. The present paper goes further in seeking a quantitative method to determine the range of domain external expansion under unknown domain bounds. The research considers the data bias phenomenon that often occurs in small data sets and provides a method for its adjustment. Beyond this, the study also compares the learning results among three types of membership functions (Bell, Trapezoid, Triangular) for data fuzzification. The results show that the proposed approach can advance the learning accuracy of a broad range of applications.

Original languageEnglish
Pages (from-to)4491-4509
Number of pages19
JournalInternational Journal of Production Research
Volume44
Issue number21
DOIs
Publication statusPublished - 2006 Nov 1

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Flexible manufacturing systems
Scheduling
Membership functions

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Management Science and Operations Research

Cite this

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