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

D. C. Li, C. Wu, F. M. Chang

研究成果: Article同行評審

19 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)4491-4509
頁數19
期刊International Journal of Production Research
44
發行號21
DOIs
出版狀態Published - 2006 十一月 1

All Science Journal Classification (ASJC) codes

  • 策略與管理
  • 管理科學與經營研究
  • 工業與製造工程

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