An Advanced Decision-Making Model for Evaluating Manufacturing Plant locations using Fuzzy Inference System

Paul, SK; Chowdhury, P; Ahsan, K; Ali, SM; Kabir, G

An Advanced Decision-Making Model for Evaluating Manufacturing Plant locations using Fuzzy Inference System

Paul, SK Chowdhury, P Ahsan, K Ali, SM Kabir, G

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2022, 191, pp. 116378-116378
Issue Date:: 2022-04

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Field	Value	Language
dc.contributor.author	Paul, SK
dc.contributor.author	Chowdhury, P
dc.contributor.author	Ahsan, K
dc.contributor.author	Ali, SM
dc.contributor.author	Kabir, G
dc.date.accessioned	2022-01-31T00:06:38Z
dc.date.available	2022-01-31T00:06:38Z
dc.date.issued	2022-04
dc.identifier.citation	Expert Systems with Applications, 2022, 191, pp. 116378-116378
dc.identifier.issn	0957-4174
dc.identifier.issn	1873-6793
dc.identifier.uri	http://hdl.handle.net/10453/153893
dc.description.abstract	Locating a manufacturing plant is a complex multi-criteria decision-making problem as it involves many tangible and intangible criteria. This paper contributes to the existing theory by integrating a qualitative Delphi and a quantitative fuzzy inference system (FIS) for developing an advanced and intelligent decision-making framework for evaluating manufacturing plant locations. The Delphi method is used to identify the most significant manufacturing plant location selection criteria. The identified major criteria are used to develop an advanced FIS framework to evaluate potential manufacturing plant locations. A real-life case is presented to demonstrate the applicability of the developed decision-making framework. This paper contributes to the literature by developing an advanced decision-making framework for evaluating manufacturing plant locations and by integrating qualitative Delphi and quantitative FIS, which can help industrial managers locate their manufacturing plant locations intelligently and accurately.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Expert Systems with Applications
dc.relation.isbasedon	10.1016/j.eswa.2021.116378
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	An Advanced Decision-Making Model for Evaluating Manufacturing Plant locations using Fuzzy Inference System
dc.type	Journal Article
utslib.citation.volume	191
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Management Discipline
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
utslib.copyright.embargo	2024-04-01T00:00:00+1000Z
dc.date.updated	2022-01-31T00:06:37Z
pubs.publication-status	Published
pubs.volume	191

Abstract:

Locating a manufacturing plant is a complex multi-criteria decision-making problem as it involves many tangible and intangible criteria. This paper contributes to the existing theory by integrating a qualitative Delphi and a quantitative fuzzy inference system (FIS) for developing an advanced and intelligent decision-making framework for evaluating manufacturing plant locations. The Delphi method is used to identify the most significant manufacturing plant location selection criteria. The identified major criteria are used to develop an advanced FIS framework to evaluate potential manufacturing plant locations. A real-life case is presented to demonstrate the applicability of the developed decision-making framework. This paper contributes to the literature by developing an advanced decision-making framework for evaluating manufacturing plant locations and by integrating qualitative Delphi and quantitative FIS, which can help industrial managers locate their manufacturing plant locations intelligently and accurately.

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http://hdl.handle.net/10453/153893