標題: | A fuzzy polynomial fitting and mathematical programming approach for enhancing the accuracy and precision of productivity forecasting |
作者: | Chen, Toly Ou, Chungwei Lin, Yu-Cheng 工業工程與管理學系 Department of Industrial Engineering and Management |
關鍵字: | Productivity;Uncertainty;Polynomial fitting;Mathematical programming;Forecasting |
公開日期: | 1-Jun-2019 |
摘要: | 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. |
URI: | http://dx.doi.org/10.1007/s10588-018-09287-w http://hdl.handle.net/11536/152236 |
ISSN: | 1381-298X |
DOI: | 10.1007/s10588-018-09287-w |
期刊: | COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY |
Volume: | 25 |
Issue: | 2 |
起始頁: | 85 |
結束頁: | 107 |
Appears in Collections: | Articles |