Abstract

We develop a back-propagation neural network (BPN) to predict the life-cycle design performance for transport packaging. The BPN is constructed and trained on the packaging design attributes to detect hidden relationships among historical or pre-existing life-cycle design data to predict a new concept design through supervised learning, by minimizing the squared difference between the actual and the predicted life-cycle design performance. To this end, the designer could use the predicted life-cycle design in a trade-off analysis and concept selection for a potential packaging design. A case example is used to illustrate the methodology.

Notes

Originally published in the Proceedings of the 2005 Northeast Decision Sciences Institute Conference, Philadelphia, Pennsylvania, March 30-April 1, 2005 (CD-ROM)

Keywords

Back-propagation neural network (BPN), life-cycle design, transport packaging

Subject Categories

Production engineering

Disciplines

Engineering

Publisher

Omnipress

Publication Date

2005

Rights Information

Copyright 2005, Surendra M. Gupta

Rights Holder

Gupta M. Surendra



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