Back to Search
Start Over
Using ensemble and metaheuristics learning principles with artificial neural networks to improve due date prediction performance.
- Source :
- International Journal of Production Research; Nov2008, Vol. 46 Issue 21, p6009-6027, 19p, 1 Diagram, 6 Charts, 4 Graphs
- Publication Year :
- 2008
-
Abstract
- One of the common and important problems in production scheduling is to quote an attractive but attainable due date for an arriving customer order. Among a wide variety of prediction methods proposed to improve due date quotation (DDQ) accuracy, artificial neural networks (ANN) are considered the most effective because of their flexible non-linear and interaction effects modelling capability. In spite of this growing use of ANNs in a DDQ context, ANNs have several intrinsic shortcomings such as instability, bias and variance problems that undermine their accuracy. In this paper, we develop an enhanced ANN-based DDQ model using machine learning, evolutionary and metaheuristics learning concepts. Computational experiments suggest that the proposed model outperforms the conventional ANN-based DDQ method under different shop environments and different training data sizes. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00207543
- Volume :
- 46
- Issue :
- 21
- Database :
- Complementary Index
- Journal :
- International Journal of Production Research
- Publication Type :
- Academic Journal
- Accession number :
- 34535710
- Full Text :
- https://doi.org/10.1080/00207540701197036