Fitting an uncertain productivity learning process using an artificial neural network approach

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Productivity is critical to the long-term competitiveness of factories. Therefore, the future productivity of factories must be estimated and enhanced. However, this is a challenging task because productivity can be improved based on a learning process that is highly uncertain. To address this problem, most existing methods fit fuzzy productivity learning processes and convert them into mathematical programming problems. However, such methods have several drawbacks, including the absence of feasible solutions, difficulty in determining a global optimum, and homogeneity in the solutions. In this study, to overcome these drawbacks, a specially designed artificial neural network (ANN) was constructed for fitting an uncertain productivity learning process. The proposed methodology was applied to an actual case of a dynamic random access memory factory. Experimental results showed that the ANN approach has a considerably higher forecasting accuracy compared with several existing methods.

Original languageEnglish
Pages (from-to)422-439
Number of pages18
JournalComputational and Mathematical Organization Theory
Volume24
Issue number3
DOIs
StatePublished - 1 Sep 2018

Keywords

  • Artificial neural network
  • Forecasting
  • Learning model
  • Productivity
  • Uncertainty

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