Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge

Der Chiang Li, Chih Sen Wu, Tung I. Tsai, Fengming M. Chang

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)

Abstract

Provided with plenty of data (experience), data mining techniques are widely used to extract suitable management skills from the data. Nevertheless, in the early stages of a manufacturing system, only rare data can be obtained, and built scheduling knowledge is usually fragile. Using small data sets, this research's purpose is improving the accuracy of machine learning for flexible manufacturing system (FMS) scheduling. The study develops a data trend estimation technique and combines it with mega-fuzzification and adaptive-network-based fuzzy inference systems (ANFIS). The results of the simulated FMS scheduling problem indicate that learning accuracy can be significantly improved using the proposed method involving a very small data set.

Original languageEnglish
Pages (from-to)1857-1869
Number of pages13
JournalComputers and Operations Research
Volume33
Issue number6
DOIs
Publication statusPublished - 2006 Jun 1

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research

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