標題: Fitting an uncertain productivity learning process using an artificial neural network approach
作者: Chen, Toly
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: Productivity;Uncertainty;Artificial neural network;Forecasting;Learning model
公開日期: 1-Sep-2018
摘要: Productivity is critical to the long-term competitiveness of factories. Therefore, the future productivity of factories must be estimated and enhanced. However, this is a challenging task because productivity can be improved based on a learning process that is highly uncertain. To address this problem, most existing methods fit fuzzy productivity learning processes and convert them into mathematical programming problems. However, such methods have several drawbacks, including the absence of feasible solutions, difficulty in determining a global optimum, and homogeneity in the solutions. In this study, to overcome these drawbacks, a specially designed artificial neural network (ANN) was constructed for fitting an uncertain productivity learning process. The proposed methodology was applied to an actual case of a dynamic random access memory factory. Experimental results showed that the ANN approach has a considerably higher forecasting accuracy compared with several existing methods.
URI: http://dx.doi.org/10.1007/s10588-017-9262-4
http://hdl.handle.net/11536/145259
ISSN: 1381-298X
DOI: 10.1007/s10588-017-9262-4
期刊: COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY
Volume: 24
起始頁: 422
結束頁: 439
Appears in Collections:Articles