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Improving classic hungarian algorithm considering uncertainty by applying for grey numbers
The Hungarian Algorithm is the most famous method for solving Linear Assignment Problems (LAP). Linear Assignment Method (LAM), as an application of LAP, is among the most popular approaches for solving Multi Criteria Decision Making (MCDM) problems. LAM assigns a priority to each alternative based...
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Published in: | International journal of research in industrial engineering (Tehran. Online) 2023-09, Vol.12 (3), p.321-336 |
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container_title | International journal of research in industrial engineering (Tehran. Online) |
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creator | Shahram Ariafar Seyed Hamed Moosavirad Ali Soltanpour |
description | The Hungarian Algorithm is the most famous method for solving Linear Assignment Problems (LAP). Linear Assignment Method (LAM), as an application of LAP, is among the most popular approaches for solving Multi Criteria Decision Making (MCDM) problems. LAM assigns a priority to each alternative based on a Decision Matrix (DM). The elements of the DM are often deterministic in MCDM. However, in the real world, the value of the elements of the DM might not be specified precisely. Hence, using interval grey numbers as the value of the DM to consider the uncertainty is reasonable. In this research, for providing a real circumstance, the classic Hungarian algorithm has been extended by using the concept of grey preference degree as the Grey Hungarian Algorithm (GHA) to solve LAM under uncertainty. To verify the proposed GHA, a real case for ranking several items of mining machinery warehouse from Sarcheshmeh Copper Complex has been solved by the GHA. Also, the same case study has been prioritized by two other methods: Grey TOPSIS and Grey VIKOR. The results of all mentioned approaches are identical, showing the validity of the proposed GHA developed in this research. |
doi_str_mv | 10.22105/riej.2023.345845.1344 |
format | article |
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Linear Assignment Method (LAM), as an application of LAP, is among the most popular approaches for solving Multi Criteria Decision Making (MCDM) problems. LAM assigns a priority to each alternative based on a Decision Matrix (DM). The elements of the DM are often deterministic in MCDM. However, in the real world, the value of the elements of the DM might not be specified precisely. Hence, using interval grey numbers as the value of the DM to consider the uncertainty is reasonable. In this research, for providing a real circumstance, the classic Hungarian algorithm has been extended by using the concept of grey preference degree as the Grey Hungarian Algorithm (GHA) to solve LAM under uncertainty. To verify the proposed GHA, a real case for ranking several items of mining machinery warehouse from Sarcheshmeh Copper Complex has been solved by the GHA. Also, the same case study has been prioritized by two other methods: Grey TOPSIS and Grey VIKOR. The results of all mentioned approaches are identical, showing the validity of the proposed GHA developed in this research.</description><identifier>ISSN: 2783-1337</identifier><identifier>EISSN: 2717-2937</identifier><identifier>DOI: 10.22105/riej.2023.345845.1344</identifier><language>eng</language><publisher>Ayandegan Institute of Higher Education</publisher><subject>grey interval number ; grey topsis ; grey vikor ; hungarian algorithm ; preference degree</subject><ispartof>International journal of research in industrial engineering (Tehran. 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Linear Assignment Method (LAM), as an application of LAP, is among the most popular approaches for solving Multi Criteria Decision Making (MCDM) problems. LAM assigns a priority to each alternative based on a Decision Matrix (DM). The elements of the DM are often deterministic in MCDM. However, in the real world, the value of the elements of the DM might not be specified precisely. Hence, using interval grey numbers as the value of the DM to consider the uncertainty is reasonable. In this research, for providing a real circumstance, the classic Hungarian algorithm has been extended by using the concept of grey preference degree as the Grey Hungarian Algorithm (GHA) to solve LAM under uncertainty. To verify the proposed GHA, a real case for ranking several items of mining machinery warehouse from Sarcheshmeh Copper Complex has been solved by the GHA. Also, the same case study has been prioritized by two other methods: Grey TOPSIS and Grey VIKOR. The results of all mentioned approaches are identical, showing the validity of the proposed GHA developed in this research.</description><subject>grey interval number</subject><subject>grey topsis</subject><subject>grey vikor</subject><subject>hungarian algorithm</subject><subject>preference degree</subject><issn>2783-1337</issn><issn>2717-2937</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNqtj01KxEAUhBtRcNC5gvQFEvvXl6xFcfau3DQvnU6mh6Q7vCRCbu9EPIKrKr4qCoqxJylKpaSwzxTDpVRC6VIbWxlbSm3MDTsokFCoWsPt7itdSK3hnh3nOTbCGDDSCnNgX6dxovwdU8_9gNfQ8_OaeqSIiePQZ4rLeeQ-pzm2gfbemnygBWNaNt5sHKdp2HbeZeI9hY2ndWwCzY_srsNhDsc_fWCn97fP14-izXhxE8URaXMZo_sFmXqHtEQ_BCdflG5FABR1bTrlqy7A9Y_SsgUAb_V_bv0AdjZmHw</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Shahram Ariafar</creator><creator>Seyed Hamed Moosavirad</creator><creator>Ali Soltanpour</creator><general>Ayandegan Institute of Higher Education</general><scope>DOA</scope></search><sort><creationdate>20230901</creationdate><title>Improving classic hungarian algorithm considering uncertainty by applying for grey numbers</title><author>Shahram Ariafar ; Seyed Hamed Moosavirad ; Ali Soltanpour</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-doaj_primary_oai_doaj_org_article_1623d0e7a0994f2c8fe7717231d777c53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>grey interval number</topic><topic>grey topsis</topic><topic>grey vikor</topic><topic>hungarian algorithm</topic><topic>preference degree</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shahram Ariafar</creatorcontrib><creatorcontrib>Seyed Hamed Moosavirad</creatorcontrib><creatorcontrib>Ali Soltanpour</creatorcontrib><collection>DOAJÂ Directory of Open Access Journals</collection><jtitle>International journal of research in industrial engineering (Tehran. Online)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shahram Ariafar</au><au>Seyed Hamed Moosavirad</au><au>Ali Soltanpour</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving classic hungarian algorithm considering uncertainty by applying for grey numbers</atitle><jtitle>International journal of research in industrial engineering (Tehran. Online)</jtitle><date>2023-09-01</date><risdate>2023</risdate><volume>12</volume><issue>3</issue><spage>321</spage><epage>336</epage><pages>321-336</pages><issn>2783-1337</issn><eissn>2717-2937</eissn><abstract>The Hungarian Algorithm is the most famous method for solving Linear Assignment Problems (LAP). Linear Assignment Method (LAM), as an application of LAP, is among the most popular approaches for solving Multi Criteria Decision Making (MCDM) problems. LAM assigns a priority to each alternative based on a Decision Matrix (DM). The elements of the DM are often deterministic in MCDM. However, in the real world, the value of the elements of the DM might not be specified precisely. Hence, using interval grey numbers as the value of the DM to consider the uncertainty is reasonable. In this research, for providing a real circumstance, the classic Hungarian algorithm has been extended by using the concept of grey preference degree as the Grey Hungarian Algorithm (GHA) to solve LAM under uncertainty. To verify the proposed GHA, a real case for ranking several items of mining machinery warehouse from Sarcheshmeh Copper Complex has been solved by the GHA. Also, the same case study has been prioritized by two other methods: Grey TOPSIS and Grey VIKOR. The results of all mentioned approaches are identical, showing the validity of the proposed GHA developed in this research.</abstract><pub>Ayandegan Institute of Higher Education</pub><doi>10.22105/riej.2023.345845.1344</doi><oa>free_for_read</oa></addata></record> |
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subjects | grey interval number grey topsis grey vikor hungarian algorithm preference degree |
title | Improving classic hungarian algorithm considering uncertainty by applying for grey numbers |
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