FMS selection under disparate level-of-satisfaction of decision making using an intelligent fuzzy-MCDM model

Arijit Bhattacharya, Ajith Abraham, Pandian Vasant

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

This chapter outlines an intelligent fuzzy multi-criteria decision-making (MCDM) model for appropriate selection of a flexible manufacturing system (FMS) in a conflicting criteria environment. A holistic methodology has been developed for finding out the “optimal FMS” from a set of candidate-FMSs. This method of trade-offs among various parameters, viz., design parameters, economic considerations, etc., affecting the FMS selection process in an MCDM environment. The proposed method calculates the global priority values (GP) for functional, design factors and other important attributes by an eigenvector method of a pair-wise comparison. These GPs are used as subjective factor measures (SFMs) in determining the selection index (SI). The proposed fuzzified methodology is equipped with the capability of determining changes in the FMS selection process that results from making changes in the parameters of the model. The model achieves balancing among criteria. Relationships among the degree of fuzziness, level-of-satisfaction and the SIs of the MCDM methodology guide decision makers under a tripartite fuzzy environment in selecting their choice of trading-off with a predetermined allowable fuzziness. The measurement of level-of-satisfaction during making the appropriate selection of FMS is carried out.

Original languageEnglish
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer International Publishing AG
Pages263-280
Number of pages18
ISBN (Print)978-0-387-76812-0
DOIs
Publication statusPublished - 1 Jan 2008
Externally publishedYes

Publication series

NameSpringer Optimization and Its Applications
Volume16
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

Keywords

  • FMS
  • Global priority
  • Intelligent fuzzy MCDM
  • Selection indices
  • Sensitivity analysis

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