Modeling an uncertain productivity learning process using an interval fuzzy methodology

Min Chi Chiu, Tin-Chih Chen*, Keng Wei Hsu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Existingmethods for forecasting theproductivity of a factory are subject to amajordrawback-the lower andupper bounds ofproductivity areusuallydeterminedby a fewextreme cases,whichunacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-basedmixed binary quadratic programming (MBQP)-ordered weighted average (OWA) approach is proposed in this study for modeling an uncertain productivity learning process. In the proposed methodology, the productivity range is divided into the inner and outer sections, which correspond to the lower and uppermembership functions of an IFN-based fuzzy productivity forecast, respectively. In thismanner, all actual values are included in the outer section, whereasmost of the values are included within the inner section to fulfill differentmanagerial purposes. According to the percentages of outlier cases, a suitable forecasting strategy can be selected. To derive the values of parameters in the IFN-based fuzzy productivity learning model, an MBQP model is proposed and optimized. Subsequently, according to the selected forecasting strategy, the OWAmethod is applied to defuzzify a fuzzy productivity forecast. The proposed methodology has been applied to the real case of a dynamic random access memory factory to evaluate its effectiveness. The experimental results indicate that the proposedmethodology was superior to several existing methods, especially in terms of mean absolute error, mean absolute percentage error, and root mean square error in evaluating the forecasting accuracy. The forecasting precision achieved using the proposedmethodology was also satisfactory.

Original languageEnglish
Article number998
Number of pages18
JournalMathematics
Volume8
Issue number6
DOIs
StatePublished - 18 Jun 2020

Keywords

  • Interval fuzzy number
  • Learning
  • Mixed binary quadratic programming
  • Ordered weighted average
  • Productivity

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