Multi-Indicators Decision for Product Design Solutions: A TOPSIS-MOGA Integrated Model
Design decisions occur in all phases of product design and largely affect the merits of the final solution, which will ultimately determine the success or failure of the product in the market. Product design is a continuous process, and a large number of existing studies have proposed decision metho...
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description | Design decisions occur in all phases of product design and largely affect the merits of the final solution, which will ultimately determine the success or failure of the product in the market. Product design is a continuous process, and a large number of existing studies have proposed decision methods and decision indicators for the characteristics of different stages of design. These methods and indicators can meet the requirements of one of the phases: demand analysis, conceptual design, or detailed design. However, further research can still be conducted on the integration of methods throughout the design phase, using intelligent design methods, and improving the design continuity and efficiency. To address this problem, a TOPSIS-MOGA-based multi-indicators decision model for product design solutions is proposed, including its product design process, decision algorithm, and selection method. First, a TOPSIS-MOGA integrated model for conceptual design and detailed design process is established, the continuity of decision-making methods is achieved by integrating decision indicators. Second, conceptual design solutions are selected through the technique for order of preference by similarity to ideal solution (TOPSIS), based on hesitant fuzzy linguistic term sets and entropy weight method. Finally, detailed design solutions are selected through a multiobjective genetic algorithm (MOGA), based on a polynomial-based response surface model and central combination experimental design method. A case study of the decision-making in the design of high-voltage electric power fittings is presented, the conceptual design phase and the detailed design phase are connected through the indicators, which demonstrates that the proposed approach is helpful in the decision-making of the product design solutions. |
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Product design is a continuous process, and a large number of existing studies have proposed decision methods and decision indicators for the characteristics of different stages of design. These methods and indicators can meet the requirements of one of the phases: demand analysis, conceptual design, or detailed design. However, further research can still be conducted on the integration of methods throughout the design phase, using intelligent design methods, and improving the design continuity and efficiency. To address this problem, a TOPSIS-MOGA-based multi-indicators decision model for product design solutions is proposed, including its product design process, decision algorithm, and selection method. First, a TOPSIS-MOGA integrated model for conceptual design and detailed design process is established, the continuity of decision-making methods is achieved by integrating decision indicators. Second, conceptual design solutions are selected through the technique for order of preference by similarity to ideal solution (TOPSIS), based on hesitant fuzzy linguistic term sets and entropy weight method. Finally, detailed design solutions are selected through a multiobjective genetic algorithm (MOGA), based on a polynomial-based response surface model and central combination experimental design method. A case study of the decision-making in the design of high-voltage electric power fittings is presented, the conceptual design phase and the detailed design phase are connected through the indicators, which demonstrates that the proposed approach is helpful in the decision-making of the product design solutions.</description><identifier>ISSN: 2227-9717</identifier><identifier>EISSN: 2227-9717</identifier><identifier>DOI: 10.3390/pr10020303</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Case studies ; Decision making ; Demand analysis ; Design improvements ; Design of experiments ; Design techniques ; Efficiency ; Entropy ; Fuzzy sets ; Genetic algorithms ; Indicators ; Knowledge ; Mathematical models ; Methods ; Multiple objective analysis ; Polynomials ; Product design ; Product development ; Production planning ; Requirements analysis ; Response surface methodology</subject><ispartof>Processes, 2022-02, Vol.10 (2), p.303</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-1176704975b63f9ceecb4239e79b59331b909942461a45ab53b64f39aa23b8b43</citedby><cites>FETCH-LOGICAL-c295t-1176704975b63f9ceecb4239e79b59331b909942461a45ab53b64f39aa23b8b43</cites><orcidid>0000-0001-8494-4516 ; 0000-0001-9319-5940</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Yu, Zeyuan</creatorcontrib><creatorcontrib>Zhao, Wu</creatorcontrib><creatorcontrib>Guo, Xin</creatorcontrib><creatorcontrib>Hu, Huicong</creatorcontrib><creatorcontrib>Fu, Chuan</creatorcontrib><creatorcontrib>Liu, Ying</creatorcontrib><title>Multi-Indicators Decision for Product Design Solutions: A TOPSIS-MOGA Integrated Model</title><title>Processes</title><description>Design decisions occur in all phases of product design and largely affect the merits of the final solution, which will ultimately determine the success or failure of the product in the market. Product design is a continuous process, and a large number of existing studies have proposed decision methods and decision indicators for the characteristics of different stages of design. These methods and indicators can meet the requirements of one of the phases: demand analysis, conceptual design, or detailed design. However, further research can still be conducted on the integration of methods throughout the design phase, using intelligent design methods, and improving the design continuity and efficiency. To address this problem, a TOPSIS-MOGA-based multi-indicators decision model for product design solutions is proposed, including its product design process, decision algorithm, and selection method. First, a TOPSIS-MOGA integrated model for conceptual design and detailed design process is established, the continuity of decision-making methods is achieved by integrating decision indicators. Second, conceptual design solutions are selected through the technique for order of preference by similarity to ideal solution (TOPSIS), based on hesitant fuzzy linguistic term sets and entropy weight method. Finally, detailed design solutions are selected through a multiobjective genetic algorithm (MOGA), based on a polynomial-based response surface model and central combination experimental design method. A case study of the decision-making in the design of high-voltage electric power fittings is presented, the conceptual design phase and the detailed design phase are connected through the indicators, which demonstrates that the proposed approach is helpful in the decision-making of the product design solutions.</description><subject>Algorithms</subject><subject>Case studies</subject><subject>Decision making</subject><subject>Demand analysis</subject><subject>Design improvements</subject><subject>Design of experiments</subject><subject>Design techniques</subject><subject>Efficiency</subject><subject>Entropy</subject><subject>Fuzzy sets</subject><subject>Genetic algorithms</subject><subject>Indicators</subject><subject>Knowledge</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Multiple objective analysis</subject><subject>Polynomials</subject><subject>Product design</subject><subject>Product development</subject><subject>Production planning</subject><subject>Requirements analysis</subject><subject>Response surface methodology</subject><issn>2227-9717</issn><issn>2227-9717</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpNkEFLAzEQhYMoWGov_oKAN2E1ySSbxlupti60tNDqdUmy2bJl3dQke_Dfu1JB5zLDm8d78CF0S8kDgCKPp0AJYQQIXKARY0xmSlJ5-e--RpMYj2QYRWEq8hF6X_dtarKiqxqrkw8RPzvbxMZ3uPYBb4OvepsGMTaHDu9826fhF5_wDO83212xy9ab5QwXXXKHoJOr8NpXrr1BV7Vuo5v87jF6W7zs56_ZarMs5rNVZpkSKaNU5pJwJYXJoVbWOWs4A-WkMkIBUKOIUpzxnGoutBFgcl6D0pqBmRoOY3R3zj0F_9m7mMqj70M3VJYsByACgNHBdX922eBjDK4uT6H50OGrpKT8QVf-oYNvrqJeDQ</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Yu, Zeyuan</creator><creator>Zhao, Wu</creator><creator>Guo, Xin</creator><creator>Hu, Huicong</creator><creator>Fu, Chuan</creator><creator>Liu, Ying</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>LK8</scope><scope>M7P</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-8494-4516</orcidid><orcidid>https://orcid.org/0000-0001-9319-5940</orcidid></search><sort><creationdate>20220201</creationdate><title>Multi-Indicators Decision for Product Design Solutions: A TOPSIS-MOGA Integrated Model</title><author>Yu, Zeyuan ; 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Product design is a continuous process, and a large number of existing studies have proposed decision methods and decision indicators for the characteristics of different stages of design. These methods and indicators can meet the requirements of one of the phases: demand analysis, conceptual design, or detailed design. However, further research can still be conducted on the integration of methods throughout the design phase, using intelligent design methods, and improving the design continuity and efficiency. To address this problem, a TOPSIS-MOGA-based multi-indicators decision model for product design solutions is proposed, including its product design process, decision algorithm, and selection method. First, a TOPSIS-MOGA integrated model for conceptual design and detailed design process is established, the continuity of decision-making methods is achieved by integrating decision indicators. 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subjects | Algorithms Case studies Decision making Demand analysis Design improvements Design of experiments Design techniques Efficiency Entropy Fuzzy sets Genetic algorithms Indicators Knowledge Mathematical models Methods Multiple objective analysis Polynomials Product design Product development Production planning Requirements analysis Response surface methodology |
title | Multi-Indicators Decision for Product Design Solutions: A TOPSIS-MOGA Integrated Model |
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