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

Toly Chen, Chungwei Ou () and Yu-Cheng Lin
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Toly Chen: National Chiao Tung University
Chungwei Ou: Chang Yuan Christian University
Yu-Cheng Lin: Overseas Chinese University

Computational and Mathematical Organization Theory, 2019, vol. 25, issue 2, No 1, 85-107

Abstract: 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.

Keywords: Productivity; Uncertainty; Polynomial fitting; Mathematical programming; Forecasting (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10588-018-09287-w

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