We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

Artificial neural networks as cost engineering methods in a collaborative manufacturing environment.

Wang, Q. (2007) 'Artificial neural networks as cost engineering methods in a collaborative manufacturing environment.', International journal of production economics., 109 (1-2). pp. 53-64.


To support the complexity of the modern manufacturing environment it is vital that cost modeling under a collaborating network of companies is developed. In this paper a cost model development process is described and a novel cost modeling technology artificial neural networks (ANN) is developed. The ANN have the ability to learn and respond in producing cost estimates for manufacturing processes and also seek to find new patterns within existing cost data for forecasting and ranking which makes intelligent computing a viable option in moving the modeling process forward. A series of experiments were undertaken to select an appropriate network structure for estimating the cost within the production network and the model is validated through a case study. Trial and error cost estimating would possibly be made easier within a linguistic and intuitive framework.

Item Type:Article
Keywords:Artificial neural networks, Cost modelling, Design of experiment.
Full text:Full text not available from this repository.
Publisher Web site:
Record Created:16 Jan 2007
Last Modified:25 Nov 2009 16:49

Social bookmarking: del.icio.usConnoteaBibSonomyCiteULikeFacebookTwitterExport: EndNote, Zotero | BibTex
Look up in GoogleScholar | Find in a UK Library