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 

作者机构：Natl Chiao Tung Univ Dept Ind Engn & Management 1001 Univ Rd Hsinchu Taiwan Chang Yuan Christian Univ Dept Ind & Syst Engn 200 Chung Pei Rd Taoyuan Taiwan Overseas Chinese Univ Dept Comp Aided Ind Design 100 Chiao Kung Rd Taichung Taiwan 

出 版 物：《COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY》 (计算与数学的组织理论)

年 卷 期：2019年第25卷第2期

页      面：85-107页

核心收录：

学科分类：07[理学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：Ministry of Science and Technology  Taiwan 

主　　题：Productivity Uncertainty Polynomial fitting Mathematical programming Forecasting 

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.