A fuzzy polynomial fitting and mathematical programming approach for enhancing the accuracy and precision of productivity forecasting

Tin-Chih Chen, Chungwei Ou*, Yu Cheng Lin

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Forecasting future productivity is a critical task to every organization. However, the existing methods for productivity forecasting have two problems. First, the logarithmic or log-sigmoid value, rather than the original value, of productivity is dealt with. Second, the objective functions are not consistent with those adopted in practice. To address these problems, a fuzzy polynomial fitting and mathematical programming (FPF-MP) approach are proposed in this study. The FPF-MP approach solves two polynomial programming problems, based on the original value of productivity, in two steps to optimize accuracy and precision of forecasting future productivity, respectively. A real case was adopted to validate the effectiveness of the proposed methodology. According to the experimental results, the proposed FPF-MP approach outperformed six existing methods in improving the forecasting accuracy and precision.

Original languageEnglish
Pages (from-to)85-107
Number of pages23
JournalComputational and Mathematical Organization Theory
Volume25
Issue number2
DOIs
StatePublished - 1 Jun 2019

Keywords

  • Forecasting
  • Mathematical programming
  • Polynomial fitting
  • Productivity
  • Uncertainty

Fingerprint Dive into the research topics of 'A fuzzy polynomial fitting and mathematical programming approach for enhancing the accuracy and precision of productivity forecasting'. Together they form a unique fingerprint.

  • Cite this