A New Approach to the Determination of Expert Weights in Multi-attribute Group Decision Making
This paper presents a new approach based on optimization model to determine the weights of experts in the multi-attribute group decision. Firstly, by minimizing the sum of differences between individual evaluations and the overall consistent evaluations of all experts, a new optimization model is es...
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| creator | Liu, Yuetong Hu, Chaolang Zhang, Shiquan Hu, Qixiao |
| description | This paper presents a new approach based on optimization model to determine
the weights of experts in the multi-attribute group decision. Firstly, by
minimizing the sum of differences between individual evaluations and the
overall consistent evaluations of all experts, a new optimization model is
established for determining expert weights. Then, rigorous proof of the unique
existence of solution is analyzed in detail, and the sequential least squares
quadratic programming algorithm is adopted to solve the optimization model.
Finally, the reasonableness of the new approach is verified by numerical
experiments, i.e., the smaller the difference between the individual
evaluations and the overall consistent evaluations, the larger the weights
assigned to the corresponding individual. |
| doi_str_mv | 10.48550/arxiv.2311.12546 |
| format | Article |
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the weights of experts in the multi-attribute group decision. Firstly, by
minimizing the sum of differences between individual evaluations and the
overall consistent evaluations of all experts, a new optimization model is
established for determining expert weights. Then, rigorous proof of the unique
existence of solution is analyzed in detail, and the sequential least squares
quadratic programming algorithm is adopted to solve the optimization model.
Finally, the reasonableness of the new approach is verified by numerical
experiments, i.e., the smaller the difference between the individual
evaluations and the overall consistent evaluations, the larger the weights
assigned to the corresponding individual.</description><identifier>DOI: 10.48550/arxiv.2311.12546</identifier><language>eng</language><subject>Mathematics - Optimization and Control</subject><creationdate>2023-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2311.12546$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2311.12546$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Yuetong</creatorcontrib><creatorcontrib>Hu, Chaolang</creatorcontrib><creatorcontrib>Zhang, Shiquan</creatorcontrib><creatorcontrib>Hu, Qixiao</creatorcontrib><title>A New Approach to the Determination of Expert Weights in Multi-attribute Group Decision Making</title><description>This paper presents a new approach based on optimization model to determine
the weights of experts in the multi-attribute group decision. Firstly, by
minimizing the sum of differences between individual evaluations and the
overall consistent evaluations of all experts, a new optimization model is
established for determining expert weights. Then, rigorous proof of the unique
existence of solution is analyzed in detail, and the sequential least squares
quadratic programming algorithm is adopted to solve the optimization model.
Finally, the reasonableness of the new approach is verified by numerical
experiments, i.e., the smaller the difference between the individual
evaluations and the overall consistent evaluations, the larger the weights
assigned to the corresponding individual.</description><subject>Mathematics - Optimization and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj81OwzAQhH3hgAoPwIl9gQQn9ubnGJVSkFq4VOJGtEk2jUUbR44D5e1JC9JIoznMaD4h7iIZ6gxRPpA7ma8wVlEURjHq5Fp8FPDK31AMg7NUd-At-I7hkT27o-nJG9uDbWF1Gth5eGez7_wIpoftdPAmIO-dqSbPsHZ2GuZibcZzZ0ufpt_fiKuWDiPf_vtC7J5Wu-VzsHlbvyyLTUBJmgQ6kRVKbHLMalSczv-kbppUp0x1PickpaomziVSVreEOUoZa1aZlFrNWoj7v9kLYDk4cyT3U55Bywuo-gVUlUy-</recordid><startdate>20231121</startdate><enddate>20231121</enddate><creator>Liu, Yuetong</creator><creator>Hu, Chaolang</creator><creator>Zhang, Shiquan</creator><creator>Hu, Qixiao</creator><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20231121</creationdate><title>A New Approach to the Determination of Expert Weights in Multi-attribute Group Decision Making</title><author>Liu, Yuetong ; Hu, Chaolang ; Zhang, Shiquan ; Hu, Qixiao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-460b505d958c53e731104dd747eac93115a33bd2905a8cfa5950024e380043043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Mathematics - Optimization and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yuetong</creatorcontrib><creatorcontrib>Hu, Chaolang</creatorcontrib><creatorcontrib>Zhang, Shiquan</creatorcontrib><creatorcontrib>Hu, Qixiao</creatorcontrib><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liu, Yuetong</au><au>Hu, Chaolang</au><au>Zhang, Shiquan</au><au>Hu, Qixiao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A New Approach to the Determination of Expert Weights in Multi-attribute Group Decision Making</atitle><date>2023-11-21</date><risdate>2023</risdate><abstract>This paper presents a new approach based on optimization model to determine
the weights of experts in the multi-attribute group decision. Firstly, by
minimizing the sum of differences between individual evaluations and the
overall consistent evaluations of all experts, a new optimization model is
established for determining expert weights. Then, rigorous proof of the unique
existence of solution is analyzed in detail, and the sequential least squares
quadratic programming algorithm is adopted to solve the optimization model.
Finally, the reasonableness of the new approach is verified by numerical
experiments, i.e., the smaller the difference between the individual
evaluations and the overall consistent evaluations, the larger the weights
assigned to the corresponding individual.</abstract><doi>10.48550/arxiv.2311.12546</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Mathematics - Optimization and Control |
| title | A New Approach to the Determination of Expert Weights in Multi-attribute Group Decision Making |
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