Introduction to soft-set theoretic solution of project selection problem
Purpose The purpose of this paper is to introduce a model of decision-making problem in multi-criteria optimization domain for project selection. The model is built by combining the soft set theory and analytic hierarchical model under fuzziness. Soft set model gives us the opportunity to use parame...
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Veröffentlicht in: | Benchmarking : an international journal 2016-01, Vol.23 (7), p.1643-1657 |
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creator | Bakshi, Tuli Sinharay, Arindam Sarkar, Bijan Sanyal, Subir Kumar |
description | Purpose
The purpose of this paper is to introduce a model of decision-making problem in multi-criteria optimization domain for project selection. The model is built by combining the soft set theory and analytic hierarchical model under fuzziness. Soft set model gives us the opportunity to use parameterization properties. Here, the authors have proved that multiple alternatives can be reduced to make the selection process computationally efficient. Here, the authors illustrate the hybrid method by means of an application of the new mathematical model of soft set theory.
Design/methodology/approach
This paper is designed to excel a decision support system with multiple criteria analysis tool, analytic hierarchy process combined with soft set theory under fuzziness.
Findings
In this paper, the authors have taken four projects P1, P2, P3 and P4. As per chosen parameters of softest theory the result of the illustrative example reveals that P2 is the best project. The ranking the authors get is in the order of P2, P3, P4 and P1. The algorithm leads the authors to maximize the proper choice in the environment of imprecise information. The main advantage of this method compare to others is that this hybrid method is very simple in terms of calculation and the computational complexity of the proposed algorithm is low.
Originality/value
This proposed decision support strategy for an intended project manager helped to take decision in the perspective environment. |
doi_str_mv | 10.1108/BIJ-05-2014-0044 |
format | Article |
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The purpose of this paper is to introduce a model of decision-making problem in multi-criteria optimization domain for project selection. The model is built by combining the soft set theory and analytic hierarchical model under fuzziness. Soft set model gives us the opportunity to use parameterization properties. Here, the authors have proved that multiple alternatives can be reduced to make the selection process computationally efficient. Here, the authors illustrate the hybrid method by means of an application of the new mathematical model of soft set theory.
Design/methodology/approach
This paper is designed to excel a decision support system with multiple criteria analysis tool, analytic hierarchy process combined with soft set theory under fuzziness.
Findings
In this paper, the authors have taken four projects P1, P2, P3 and P4. As per chosen parameters of softest theory the result of the illustrative example reveals that P2 is the best project. The ranking the authors get is in the order of P2, P3, P4 and P1. The algorithm leads the authors to maximize the proper choice in the environment of imprecise information. The main advantage of this method compare to others is that this hybrid method is very simple in terms of calculation and the computational complexity of the proposed algorithm is low.
Originality/value
This proposed decision support strategy for an intended project manager helped to take decision in the perspective environment.</description><identifier>ISSN: 1463-5771</identifier><identifier>EISSN: 1758-4094</identifier><identifier>DOI: 10.1108/BIJ-05-2014-0044</identifier><language>eng</language><publisher>Bradford: Emerald Group Publishing Limited</publisher><subject>Algorithms ; Alternatives ; Analytic hierarchy process ; Data envelopment analysis ; Decision analysis ; Decision making ; Decision support systems ; Fuzzy sets ; Hierarchies ; Linear programming ; Mathematical models ; Mathematical programming ; Multiple criterion ; Optimization ; Parameterization ; Preferences ; Project evaluation ; Set theory ; Support systems ; Utility functions</subject><ispartof>Benchmarking : an international journal, 2016-01, Vol.23 (7), p.1643-1657</ispartof><rights>Emerald Group Publishing Limited</rights><rights>Emerald Group Publishing Limited 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c342t-6cb3fb8407db44e22b583ac87ee18618ad7161bdfbb2476337509f725c37771d3</citedby><cites>FETCH-LOGICAL-c342t-6cb3fb8407db44e22b583ac87ee18618ad7161bdfbb2476337509f725c37771d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.emerald.com/insight/content/doi/10.1108/BIJ-05-2014-0044/full/html$$EHTML$$P50$$Gemerald$$H</linktohtml><link.rule.ids>314,780,784,966,11634,21694,27923,27924,52688,53243</link.rule.ids></links><search><creatorcontrib>Bakshi, Tuli</creatorcontrib><creatorcontrib>Sinharay, Arindam</creatorcontrib><creatorcontrib>Sarkar, Bijan</creatorcontrib><creatorcontrib>Sanyal, Subir Kumar</creatorcontrib><title>Introduction to soft-set theoretic solution of project selection problem</title><title>Benchmarking : an international journal</title><description>Purpose
The purpose of this paper is to introduce a model of decision-making problem in multi-criteria optimization domain for project selection. The model is built by combining the soft set theory and analytic hierarchical model under fuzziness. Soft set model gives us the opportunity to use parameterization properties. Here, the authors have proved that multiple alternatives can be reduced to make the selection process computationally efficient. Here, the authors illustrate the hybrid method by means of an application of the new mathematical model of soft set theory.
Design/methodology/approach
This paper is designed to excel a decision support system with multiple criteria analysis tool, analytic hierarchy process combined with soft set theory under fuzziness.
Findings
In this paper, the authors have taken four projects P1, P2, P3 and P4. As per chosen parameters of softest theory the result of the illustrative example reveals that P2 is the best project. The ranking the authors get is in the order of P2, P3, P4 and P1. The algorithm leads the authors to maximize the proper choice in the environment of imprecise information. The main advantage of this method compare to others is that this hybrid method is very simple in terms of calculation and the computational complexity of the proposed algorithm is low.
Originality/value
This proposed decision support strategy for an intended project manager helped to take decision in the perspective environment.</description><subject>Algorithms</subject><subject>Alternatives</subject><subject>Analytic hierarchy process</subject><subject>Data envelopment analysis</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Decision support systems</subject><subject>Fuzzy sets</subject><subject>Hierarchies</subject><subject>Linear programming</subject><subject>Mathematical models</subject><subject>Mathematical programming</subject><subject>Multiple criterion</subject><subject>Optimization</subject><subject>Parameterization</subject><subject>Preferences</subject><subject>Project evaluation</subject><subject>Set theory</subject><subject>Support systems</subject><subject>Utility functions</subject><issn>1463-5771</issn><issn>1758-4094</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptkL1PAzEMxSMEEqWwM57EHOp8Xa4jVNAWVWKBObrkHNHq2pQkN_Dfk3IsSEy27Pf8rB8htwzuGYNm9rh-oaAoByYpgJRnZMK0aqiEuTwvvawFVVqzS3KV0g4AatbwCVmtDzmGbnB5Gw5VDlUKPtOEucofGCLmrSujfvhZB18dY9ihy1XCHkdPmdge99fkwrd9wpvfOiXvz09vixXdvC7Xi4cNdULyTGtnhbeNBN1ZKZFzqxrRukYjsqa81Haa1cx23loudS2EVjD3misndPm-E1NyN94tuZ8Dpmx2YYiHEmnKAV5EUomiglHlYkgpojfHuN238cswMCdepvAyoMyJlznxKpbZaME9xrbv_nP8ISy-AWCNa4Q</recordid><startdate>20160101</startdate><enddate>20160101</enddate><creator>Bakshi, Tuli</creator><creator>Sinharay, Arindam</creator><creator>Sarkar, Bijan</creator><creator>Sanyal, Subir Kumar</creator><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>7TA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X5</scope><scope>7XB</scope><scope>8AO</scope><scope>8FD</scope><scope>8FI</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>F~G</scope><scope>JG9</scope><scope>K6~</scope><scope>K8~</scope><scope>L.-</scope><scope>L.0</scope><scope>M0C</scope><scope>M0T</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>20160101</creationdate><title>Introduction to soft-set theoretic solution of project selection problem</title><author>Bakshi, Tuli ; Sinharay, Arindam ; Sarkar, Bijan ; Sanyal, Subir Kumar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c342t-6cb3fb8407db44e22b583ac87ee18618ad7161bdfbb2476337509f725c37771d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Alternatives</topic><topic>Analytic hierarchy process</topic><topic>Data envelopment analysis</topic><topic>Decision analysis</topic><topic>Decision making</topic><topic>Decision support systems</topic><topic>Fuzzy sets</topic><topic>Hierarchies</topic><topic>Linear programming</topic><topic>Mathematical models</topic><topic>Mathematical programming</topic><topic>Multiple criterion</topic><topic>Optimization</topic><topic>Parameterization</topic><topic>Preferences</topic><topic>Project evaluation</topic><topic>Set theory</topic><topic>Support systems</topic><topic>Utility functions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bakshi, Tuli</creatorcontrib><creatorcontrib>Sinharay, Arindam</creatorcontrib><creatorcontrib>Sarkar, Bijan</creatorcontrib><creatorcontrib>Sanyal, Subir Kumar</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Materials Business File</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Entrepreneurship Database</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Materials Research Database</collection><collection>ProQuest Business Collection</collection><collection>DELNET Management Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ABI/INFORM Global</collection><collection>Healthcare Administration Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Benchmarking : an international journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bakshi, Tuli</au><au>Sinharay, Arindam</au><au>Sarkar, Bijan</au><au>Sanyal, Subir Kumar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Introduction to soft-set theoretic solution of project selection problem</atitle><jtitle>Benchmarking : an international journal</jtitle><date>2016-01-01</date><risdate>2016</risdate><volume>23</volume><issue>7</issue><spage>1643</spage><epage>1657</epage><pages>1643-1657</pages><issn>1463-5771</issn><eissn>1758-4094</eissn><abstract>Purpose
The purpose of this paper is to introduce a model of decision-making problem in multi-criteria optimization domain for project selection. The model is built by combining the soft set theory and analytic hierarchical model under fuzziness. Soft set model gives us the opportunity to use parameterization properties. Here, the authors have proved that multiple alternatives can be reduced to make the selection process computationally efficient. Here, the authors illustrate the hybrid method by means of an application of the new mathematical model of soft set theory.
Design/methodology/approach
This paper is designed to excel a decision support system with multiple criteria analysis tool, analytic hierarchy process combined with soft set theory under fuzziness.
Findings
In this paper, the authors have taken four projects P1, P2, P3 and P4. As per chosen parameters of softest theory the result of the illustrative example reveals that P2 is the best project. The ranking the authors get is in the order of P2, P3, P4 and P1. The algorithm leads the authors to maximize the proper choice in the environment of imprecise information. The main advantage of this method compare to others is that this hybrid method is very simple in terms of calculation and the computational complexity of the proposed algorithm is low.
Originality/value
This proposed decision support strategy for an intended project manager helped to take decision in the perspective environment.</abstract><cop>Bradford</cop><pub>Emerald Group Publishing Limited</pub><doi>10.1108/BIJ-05-2014-0044</doi><tpages>15</tpages></addata></record> |
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subjects | Algorithms Alternatives Analytic hierarchy process Data envelopment analysis Decision analysis Decision making Decision support systems Fuzzy sets Hierarchies Linear programming Mathematical models Mathematical programming Multiple criterion Optimization Parameterization Preferences Project evaluation Set theory Support systems Utility functions |
title | Introduction to soft-set theoretic solution of project selection problem |
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