Product modular design incorporating life cycle issues - Group Genetic Algorithm (GGA) based method
Traditional design methods lead to serious environmental problems because of the oversight of life cycle issues such as recycling. For solving these problems, a new modular design method is proposed with the considerations of not only the traditional function related attributes, but also the life cy...
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Veröffentlicht in: | Journal of cleaner production 2011-06, Vol.19 (9), p.1016-1032 |
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container_title | Journal of cleaner production |
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creator | Yu, Suiran Yang, Qingyan Tao, Jing Tian, Xia Yin, Fengfu |
description | Traditional design methods lead to serious environmental problems because of the oversight of life cycle issues such as recycling. For solving these problems, a new modular design method is proposed with the considerations of not only the traditional function related attributes, but also the life cycle related ones. These attributes form what we call Modular Driving Forces (MDFs). The proposed method first determines what MDFs should be included and what their weights should be. Then the component to component relations with each specific MDF are generated and expressed in a matrix. After that, the comprehensive relations between components with different MDFs are established with the introduction of a comprehensive relation matrix for further modular optimization. Each element in the comprehensive matrix denotes the relation of every two components affected by all the MDFs. Finally, Group Genetic Algorithm (GGA) is employed to conduct modular optimization. The modular object adaptive function constructed for GGA optimization is to maximize the interactions between components within modules. The proposed method is explained by a case study of a refrigerator. Sensitivity analysis shows that the proposed method is robust. |
doi_str_mv | 10.1016/j.jclepro.2011.02.006 |
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For solving these problems, a new modular design method is proposed with the considerations of not only the traditional function related attributes, but also the life cycle related ones. These attributes form what we call Modular Driving Forces (MDFs). The proposed method first determines what MDFs should be included and what their weights should be. Then the component to component relations with each specific MDF are generated and expressed in a matrix. After that, the comprehensive relations between components with different MDFs are established with the introduction of a comprehensive relation matrix for further modular optimization. Each element in the comprehensive matrix denotes the relation of every two components affected by all the MDFs. Finally, Group Genetic Algorithm (GGA) is employed to conduct modular optimization. The modular object adaptive function constructed for GGA optimization is to maximize the interactions between components within modules. 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Sensitivity analysis shows that the proposed method is robust.</description><identifier>ISSN: 0959-6526</identifier><identifier>EISSN: 1879-1786</identifier><identifier>DOI: 10.1016/j.jclepro.2011.02.006</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Genetic algorithms ; Group genetic algorithm (GGA) ; Life cycle ; Life cycle engineering ; MDF ; Modular ; Modular design ; Modular driving forces (MDFs) ; Modularity ; Optimization ; Refrigerators ; Sensitivity analysis</subject><ispartof>Journal of cleaner production, 2011-06, Vol.19 (9), p.1016-1032</ispartof><rights>2011 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c440t-64aaf295a7a33f9041ba7ebc559421512e7e401a171f51d00bf90f3abe9468483</citedby><cites>FETCH-LOGICAL-c440t-64aaf295a7a33f9041ba7ebc559421512e7e401a171f51d00bf90f3abe9468483</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jclepro.2011.02.006$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Yu, Suiran</creatorcontrib><creatorcontrib>Yang, Qingyan</creatorcontrib><creatorcontrib>Tao, Jing</creatorcontrib><creatorcontrib>Tian, Xia</creatorcontrib><creatorcontrib>Yin, Fengfu</creatorcontrib><title>Product modular design incorporating life cycle issues - Group Genetic Algorithm (GGA) based method</title><title>Journal of cleaner production</title><description>Traditional design methods lead to serious environmental problems because of the oversight of life cycle issues such as recycling. 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The proposed method is explained by a case study of a refrigerator. Sensitivity analysis shows that the proposed method is robust.</description><subject>Genetic algorithms</subject><subject>Group genetic algorithm (GGA)</subject><subject>Life cycle</subject><subject>Life cycle engineering</subject><subject>MDF</subject><subject>Modular</subject><subject>Modular design</subject><subject>Modular driving forces (MDFs)</subject><subject>Modularity</subject><subject>Optimization</subject><subject>Refrigerators</subject><subject>Sensitivity analysis</subject><issn>0959-6526</issn><issn>1879-1786</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNqFkMFO3DAQhi1EJRbaR0DyDTgkzCR2nJzQCtGAhASH9mw5zmTxKokXO4vE29er5VxO_-Wbf2Y-xi4RcgSsbrf51o60Cz4vADGHIgeoTtgKa9VkqOrqlK2gkU1WyaI6Y-cxbgFQgRIrZl-D7_d24VOK0QTeU3SbmbvZ-rDzwSxu3vDRDcTtZ9rCXYx7ijzjbfD7HW9ppsVZvh43PrjlbeLXbbu-4Z2J1POJljff_2Q_BjNG-vWVF-zv74c_94_Z80v7dL9-zqwQsGSVMGYoGmmUKcuhAYGdUdRZKRtRoMSCFAlAgwoHiT1Al6ChNB01oqpFXV6wq2NvUvGejlz05KKlcTQz-X3UddXIuqwBE3n9XxKVUghloWRC5RG1wccYaNC74CYTPjWCPujXW_2lXx_0ayh00p_m7o5zlD7-cBR0tI5mS70LZBfde_dNwz_QaZAx</recordid><startdate>20110601</startdate><enddate>20110601</enddate><creator>Yu, Suiran</creator><creator>Yang, Qingyan</creator><creator>Tao, Jing</creator><creator>Tian, Xia</creator><creator>Yin, Fengfu</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QQ</scope><scope>7SU</scope><scope>7TA</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>JG9</scope><scope>7ST</scope><scope>7TV</scope><scope>7U6</scope><scope>SOI</scope></search><sort><creationdate>20110601</creationdate><title>Product modular design incorporating life cycle issues - Group Genetic Algorithm (GGA) based method</title><author>Yu, Suiran ; Yang, Qingyan ; Tao, Jing ; Tian, Xia ; Yin, Fengfu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c440t-64aaf295a7a33f9041ba7ebc559421512e7e401a171f51d00bf90f3abe9468483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Genetic algorithms</topic><topic>Group genetic algorithm (GGA)</topic><topic>Life cycle</topic><topic>Life cycle engineering</topic><topic>MDF</topic><topic>Modular</topic><topic>Modular design</topic><topic>Modular driving forces (MDFs)</topic><topic>Modularity</topic><topic>Optimization</topic><topic>Refrigerators</topic><topic>Sensitivity analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yu, Suiran</creatorcontrib><creatorcontrib>Yang, Qingyan</creatorcontrib><creatorcontrib>Tao, Jing</creatorcontrib><creatorcontrib>Tian, Xia</creatorcontrib><creatorcontrib>Yin, Fengfu</creatorcontrib><collection>CrossRef</collection><collection>Ceramic Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Materials Research Database</collection><collection>Environment Abstracts</collection><collection>Pollution Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environment Abstracts</collection><jtitle>Journal of cleaner production</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yu, Suiran</au><au>Yang, Qingyan</au><au>Tao, Jing</au><au>Tian, Xia</au><au>Yin, Fengfu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Product modular design incorporating life cycle issues - Group Genetic Algorithm (GGA) based method</atitle><jtitle>Journal of cleaner production</jtitle><date>2011-06-01</date><risdate>2011</risdate><volume>19</volume><issue>9</issue><spage>1016</spage><epage>1032</epage><pages>1016-1032</pages><issn>0959-6526</issn><eissn>1879-1786</eissn><abstract>Traditional design methods lead to serious environmental problems because of the oversight of life cycle issues such as recycling. For solving these problems, a new modular design method is proposed with the considerations of not only the traditional function related attributes, but also the life cycle related ones. These attributes form what we call Modular Driving Forces (MDFs). The proposed method first determines what MDFs should be included and what their weights should be. Then the component to component relations with each specific MDF are generated and expressed in a matrix. After that, the comprehensive relations between components with different MDFs are established with the introduction of a comprehensive relation matrix for further modular optimization. Each element in the comprehensive matrix denotes the relation of every two components affected by all the MDFs. Finally, Group Genetic Algorithm (GGA) is employed to conduct modular optimization. The modular object adaptive function constructed for GGA optimization is to maximize the interactions between components within modules. 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subjects | Genetic algorithms Group genetic algorithm (GGA) Life cycle Life cycle engineering MDF Modular Modular design Modular driving forces (MDFs) Modularity Optimization Refrigerators Sensitivity analysis |
title | Product modular design incorporating life cycle issues - Group Genetic Algorithm (GGA) based method |
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