Parameter estimation for optimal disassembly planning

David E. Grochowski, Ying Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Due to the rapid increase of technological waste in recent years, it has become necessary to find ways of handling the waste in an economically sound and environmentally benign manner. In order to do so, many groups are attempting to disassemble obsolete products in order to reuse or recycle the various components and/or materials such products are comprised. To ease in the disassembly procedure of these products, this paper describes an expert system, consisting of a Disassembly Petri network (DPN) and a Hybrid Bayesian network (HBN), for optimal disassembly planning. The DPN, the HBN, the interaction between the DPN and the HBN, and the use of inference with the HBN are briefly discussed. Specifically, the paper focuses on ascertaining the parameters of the nodes in the HBN with the use of data collected during the disassembly process and a method for the structure learning of nodes which influence the logistic nodes in the HBN.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
Pages2490-2495
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 - Montreal, QC, Canada
Duration: Oct 7 2007Oct 10 2007

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Other

Other2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
CountryCanada
CityMontreal, QC
Period10/7/0710/10/07

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Parameter estimation for optimal disassembly planning'. Together they form a unique fingerprint.

Cite this