eprintid: 276
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/02/76
datestamp: 2023-11-09 15:15:54
lastmod: 2023-11-09 15:15:54
status_changed: 2023-11-09 15:13:42
type: article
metadata_visibility: show
creators_name: Bhattacharya, A.
creators_name: Abraham, A.
creators_name: Vasant, P.
creators_name: Grosan, C.
title: Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker
ispublished: pub
note: cited By 46
abstract: This paper proposes the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) in selecting the best flexible manufacturing systems (FMS) from a group of candidate FMSs. Multi-criteria decision-making (MCDM) methodology using an improved S-shaped membership function has been developed for finding out the "best candidate FMS alternative" from a set of candidate-FMSs. The MCDM model trade-offs among various parameters, viz., design parameters, economic considerations, etc., affecting the FMS selection process under multiple, conflicting-in-nature criteria environment. The selection of FMS is made according to the error output of the results found from the proposed MCDM model.
date: 2007
publisher: IJICIC Editorial Office
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049061772&partnerID=40&md5=828f2e8eaeb8da9b97e57e6d254e667f
full_text_status: none
publication: International Journal of Innovative Computing, Information and Control
volume: 3
number: 1
pagerange: 131-140
refereed: TRUE
issn: 13494198
citation:   Bhattacharya, A. and Abraham, A. and Vasant, P. and Grosan, C.  (2007) Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker.  International Journal of Innovative Computing, Information and Control, 3 (1).  pp. 131-140.  ISSN 13494198