Multi-person and multi-criteria decision making with the induced probabilistic ordered weighted average distance

Casanovas, M; Torres-Martínez, A; Merigó, JM

Multi-person and multi-criteria decision making with the induced probabilistic ordered weighted average distance

Casanovas, M Torres-Martínez, A Merigó, JM

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Publisher:: Springer (part of Springer Nature)
Publication Type:: Journal Article
Citation:: Soft Computing, 2020, 24, (2), pp. 1435-1446
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Casanovas, M
dc.contributor.author	Torres-Martínez, A
dc.contributor.author	Merigó, JM
dc.date.accessioned	2021-02-23T11:56:43Z
dc.date.available	2021-02-23T11:56:43Z
dc.date.issued	2020-01-01
dc.identifier.citation	Soft Computing, 2020, 24, (2), pp. 1435-1446
dc.identifier.issn	1432-7643
dc.identifier.issn	1433-7479
dc.identifier.uri	http://hdl.handle.net/10453/146338
dc.description.abstract	© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a new approach for selecting suppliers of products or services, specifically with respect to complex decisions that require evaluating different business characteristics to ensure their suitability and to meet the conditions defined in the recruitment process. To address this type of problem, this study presents the multi-person multi-criteria induced ordered weighted average distance (MP-MC-IOWAD) operator, which is an extension of the OWA operators that includes the notion of distances to multiple criteria and expert valuations. Thus, this work introduces new distance measures that can aggregate the information with probabilistic information and consider the attitudinal character of the decision maker. Further extensions are developed using probabilities to form the induced probabilistic ordered weighted average distance (IPOWAD) operator. An example in the management of insurance policies is presented, where the selection of insurance companies is very complex and requires the consideration of subjective criteria by experts in decision making.
dc.language	en
dc.publisher	Springer (part of Springer Nature)
dc.relation.ispartof	Soft Computing
dc.relation.isbasedon	10.1007/s00500-019-03977-6
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Multi-person and multi-criteria decision making with the induced probabilistic ordered weighted average distance
dc.type	Journal Article
utslib.citation.volume	24
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2021-02-23T11:56:42Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	24
utslib.citation.issue	2

Abstract:

© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a new approach for selecting suppliers of products or services, specifically with respect to complex decisions that require evaluating different business characteristics to ensure their suitability and to meet the conditions defined in the recruitment process. To address this type of problem, this study presents the multi-person multi-criteria induced ordered weighted average distance (MP-MC-IOWAD) operator, which is an extension of the OWA operators that includes the notion of distances to multiple criteria and expert valuations. Thus, this work introduces new distance measures that can aggregate the information with probabilistic information and consider the attitudinal character of the decision maker. Further extensions are developed using probabilities to form the induced probabilistic ordered weighted average distance (IPOWAD) operator. An example in the management of insurance policies is presented, where the selection of insurance companies is very complex and requires the consideration of subjective criteria by experts in decision making.

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http://hdl.handle.net/10453/146338