Improving quality function deployment analysis with the cloud MULTIMOORA method
Quality function deployment (QFD) is a quality guarantee method extensively used in various industries, which can help enterprises shorten the product design period and enhance the manufacturing and managing work. The task of selecting important engineering characteristics (ECs) in QFD is crucial an...
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Veröffentlicht in: | International transactions in operational research 2020-05, Vol.27 (3), p.1600-1621 |
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description | Quality function deployment (QFD) is a quality guarantee method extensively used in various industries, which can help enterprises shorten the product design period and enhance the manufacturing and managing work. The task of selecting important engineering characteristics (ECs) in QFD is crucial and often involves multiple customer requirements (CRs). In this paper, a modified multi‐objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method based on cloud model theory (called C‐MULTIMOORA) is developed to determine the ranking order of ECs in QFD. First, the linguistic assessments provided by decision makers are transformed into normal clouds and aggregated by the cloud weighted averaging operator. Then, the weights of CRs are determined based on a maximizing deviation method with incomplete weight information. Finally, the importance of ECs is obtained using the C‐MULTIMOORA method. An empirical case conducted in an electric vehicle manufacturing organization is provided together with a comparative analysis to validate the advantages of our proposed QFD model. |
doi_str_mv | 10.1111/itor.12484 |
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The task of selecting important engineering characteristics (ECs) in QFD is crucial and often involves multiple customer requirements (CRs). In this paper, a modified multi‐objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method based on cloud model theory (called C‐MULTIMOORA) is developed to determine the ranking order of ECs in QFD. First, the linguistic assessments provided by decision makers are transformed into normal clouds and aggregated by the cloud weighted averaging operator. Then, the weights of CRs are determined based on a maximizing deviation method with incomplete weight information. Finally, the importance of ECs is obtained using the C‐MULTIMOORA method. An empirical case conducted in an electric vehicle manufacturing organization is provided together with a comparative analysis to validate the advantages of our proposed QFD model.</description><identifier>ISSN: 0969-6016</identifier><identifier>EISSN: 1475-3995</identifier><identifier>DOI: 10.1111/itor.12484</identifier><language>eng</language><publisher>Oxford: Blackwell Publishing Ltd</publisher><subject>cloud model ; Clouds ; electric vehicle ; Empirical analysis ; Fuel consumption ; incomplete weight information ; MULTIMOORA method ; Operations research ; Optimization ; Product design ; Quality function deployment ; quality function deployment (QFD)</subject><ispartof>International transactions in operational research, 2020-05, Vol.27 (3), p.1600-1621</ispartof><rights>2017 The Authors. 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The task of selecting important engineering characteristics (ECs) in QFD is crucial and often involves multiple customer requirements (CRs). In this paper, a modified multi‐objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method based on cloud model theory (called C‐MULTIMOORA) is developed to determine the ranking order of ECs in QFD. First, the linguistic assessments provided by decision makers are transformed into normal clouds and aggregated by the cloud weighted averaging operator. Then, the weights of CRs are determined based on a maximizing deviation method with incomplete weight information. Finally, the importance of ECs is obtained using the C‐MULTIMOORA method. An empirical case conducted in an electric vehicle manufacturing organization is provided together with a comparative analysis to validate the advantages of our proposed QFD model.</description><subject>cloud model</subject><subject>Clouds</subject><subject>electric vehicle</subject><subject>Empirical analysis</subject><subject>Fuel consumption</subject><subject>incomplete weight information</subject><subject>MULTIMOORA method</subject><subject>Operations research</subject><subject>Optimization</subject><subject>Product design</subject><subject>Quality function deployment</subject><subject>quality function deployment (QFD)</subject><issn>0969-6016</issn><issn>1475-3995</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kF1LwzAYhYMoOKc3_oKAd0JnPtqmuRzDj8JGYWzXIUsyl9E2W5Iq_fd21mvPzbl53pfDA8AjRjM85MVG52eYpEV6BSY4ZVlCOc-uwQTxnCc5wvktuAvhiBDCGWYTUJXNybsv237CcydrG3u471oVrWuhNqfa9Y1pI5StrPtgA_y28QDjwUBVu07D1Xa5KVdVtZ7DxsSD0_fgZi_rYB7-egq2b6-bxUeyrN7LxXyZKIpwmuTYEM0YylhGCcqZwhJJTvUuV5rtTCZNShktdopxxI3WmNMCY0VZqhFBWtMpeBr_DuvPnQlRHF3nh5VBEEoKkuWckYF6HinlXQje7MXJ20b6XmAkLsLERZj4FTbAeIS_bW36f0hRbqr1ePMDqL5tXg</recordid><startdate>202005</startdate><enddate>202005</enddate><creator>Wu, Song‐Man</creator><creator>You, Xiao‐Yue</creator><creator>Liu, Hu‐Chen</creator><creator>Wang, Li‐En</creator><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>202005</creationdate><title>Improving quality function deployment analysis with the cloud MULTIMOORA method</title><author>Wu, Song‐Man ; You, Xiao‐Yue ; Liu, Hu‐Chen ; Wang, Li‐En</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3014-61e2d77057532067c1a0a93db6cd7be5ae43738bc7909edd193811c374d020dd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>cloud model</topic><topic>Clouds</topic><topic>electric vehicle</topic><topic>Empirical analysis</topic><topic>Fuel consumption</topic><topic>incomplete weight information</topic><topic>MULTIMOORA method</topic><topic>Operations research</topic><topic>Optimization</topic><topic>Product design</topic><topic>Quality function deployment</topic><topic>quality function deployment (QFD)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Song‐Man</creatorcontrib><creatorcontrib>You, Xiao‐Yue</creatorcontrib><creatorcontrib>Liu, Hu‐Chen</creatorcontrib><creatorcontrib>Wang, Li‐En</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International transactions in operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Song‐Man</au><au>You, Xiao‐Yue</au><au>Liu, Hu‐Chen</au><au>Wang, Li‐En</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving quality function deployment analysis with the cloud MULTIMOORA method</atitle><jtitle>International transactions in operational research</jtitle><date>2020-05</date><risdate>2020</risdate><volume>27</volume><issue>3</issue><spage>1600</spage><epage>1621</epage><pages>1600-1621</pages><issn>0969-6016</issn><eissn>1475-3995</eissn><abstract>Quality function deployment (QFD) is a quality guarantee method extensively used in various industries, which can help enterprises shorten the product design period and enhance the manufacturing and managing work. The task of selecting important engineering characteristics (ECs) in QFD is crucial and often involves multiple customer requirements (CRs). In this paper, a modified multi‐objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method based on cloud model theory (called C‐MULTIMOORA) is developed to determine the ranking order of ECs in QFD. First, the linguistic assessments provided by decision makers are transformed into normal clouds and aggregated by the cloud weighted averaging operator. Then, the weights of CRs are determined based on a maximizing deviation method with incomplete weight information. Finally, the importance of ECs is obtained using the C‐MULTIMOORA method. An empirical case conducted in an electric vehicle manufacturing organization is provided together with a comparative analysis to validate the advantages of our proposed QFD model.</abstract><cop>Oxford</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/itor.12484</doi><tpages>22</tpages></addata></record> |
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subjects | cloud model Clouds electric vehicle Empirical analysis Fuel consumption incomplete weight information MULTIMOORA method Operations research Optimization Product design Quality function deployment quality function deployment (QFD) |
title | Improving quality function deployment analysis with the cloud MULTIMOORA method |
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