Title: A Bayesian decision support system for vehicle component recovery

Authors: Ajith Kumar Parlikad, Duncan McFarlane

Addresses: Institute for Manufacturing, Cambridge University Engineering Department, 17 Charles Babbage Road, Cambridge CB3 0FS, UK. ' Institute for Manufacturing, Cambridge University Engineering Department, 17 Charles Babbage Road, Cambridge CB3 0FS, UK

Abstract: This paper presents a decision support system (DSS) whose core is based on Bayes| networks and influence diagrams that helps remanufacturers to choose the best product recovery option on the basis of the information provided by emerging technologies such as RFID tags and sensor networks. Such technologies can have significant impact on the effectiveness with which product information is generated and shared among the various actors in the product lifecycle. As an illustration, we show how such a DSS can be used to improve the effectiveness of decisions made by vehicle remanufacturers where one needs to select reusable components for disassembly before shredding an end-of-life vehicle.

Keywords: product recovery; end-of-life vehicles; ELV; decision support systems; DSS; Bayesian networks; influence diagrams; EOL vehicles; vehicle components; component recovery; automotive remanufacturing; sustainable manufacturing; sustainability; product information; RFID tags; radio frequency identification; sensor networks; reusable components; disassembly.

DOI: 10.1504/IJSM.2009.031362

International Journal of Sustainable Manufacturing, 2009 Vol.1 No.4, pp.415 - 436

Published online: 01 Feb 2010 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article