Forecasting the unit cost of a DRAM product using a layered partial-consensus fuzzy collaborative forecasting approach

Tin-Chih Toly Chen, Hsin-Chieh Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

A layered partial-consensus fuzzy collaborative forecasting approach is proposed in this study to forecast the unit cost of a dynamic random access memory (DRAM) product. In the layered partial-consensus fuzzy collaborative forecasting approach, the partial-consensus fuzzy intersection (PCFI) operator is applied instead of the prevalent fuzzy intersection (FI) operator to aggregate the fuzzy forecasts by experts. In this way, some meaningful information, such as the suitable number of experts, can be obtained through observing changes in the PCFI result when the number of experts varies. After applying the layered partial-consensus fuzzy collaborative forecasting approach to a real case, the experimental results revealed that the layered partial-consensus fuzzy collaborative forecasting approach outperformed three existing methods. The most significant advantage was up to 13%.

Original languageEnglish
Number of pages14
JournalComplex & intelligent systems
DOIs
StatePublished - 9 May 2020

Keywords

  • Fuzzy collaborative forecasting
  • Dynamic random access memory
  • Layered partial consensus
  • HYBRID FUZZY
  • INTELLIGENCE APPROACH
  • LINEAR-REGRESSION
  • C-MEANS
  • TIME
  • OPTIMIZATION
  • MODELS
  • SYSTEM

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