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Open AccessArticle

Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology

1
Department of Industrial Engineering and Management, National Chin-Yi University of Technology, 57, Sec. 2, Zhongshan Rd., Taiping, Taichung City 411, Taiwan
2
Department of Industrial Engineering and Management, National Chiao Tung University, 1001, University Road, Hsinchu 300, Taiwan
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(6), 998; https://doi.org/10.3390/math8060998
Received: 20 May 2020 / Revised: 15 June 2020 / Accepted: 16 June 2020 / Published: 18 June 2020
(This article belongs to the Special Issue Applications of Fuzzy Optimization and Fuzzy Decision Making)
Existing methods for forecasting the productivity of a factory are subject to a major drawback—the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mixed 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 upper membership functions of an IFN-based fuzzy productivity forecast, respectively. In this manner, all actual values are included in the outer section, whereas most of the values are included within the inner section to fulfill different managerial 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 OWA method 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 proposed methodology 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 proposed methodology was also satisfactory. View Full-Text
Keywords: productivity; learning; interval fuzzy number; mixed binary quadratic programming; ordered weighted average productivity; learning; interval fuzzy number; mixed binary quadratic programming; ordered weighted average
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Chiu, M.-C.; Chen, T.-C.T.; Hsu, K.-W. Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology. Mathematics 2020, 8, 998.

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