A Large Group Emergency Decision-Making Method Based on Uncertain Linguistic Cloud Similarity Method
In recent years, the consensus-reaching process of large group decision making has attracted much attention in the research society, especially in emergency environment area. However, the decision information is always limited and inaccurate. The trust relationship among decision makers has been pro...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/27/6/101 |
| _version_ | 1797456486557810688 |
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| author | Gang Chen Lihua Wei Jiangyue Fu Chengjiang Li Gang Zhao |
| author_facet | Gang Chen Lihua Wei Jiangyue Fu Chengjiang Li Gang Zhao |
| author_sort | Gang Chen |
| collection | DOAJ |
| description | In recent years, the consensus-reaching process of large group decision making has attracted much attention in the research society, especially in emergency environment area. However, the decision information is always limited and inaccurate. The trust relationship among decision makers has been proven to exert important impacts on group consensus. In this study, we proposed a novel uncertain linguistic cloud similarity method based on trust update and the opinion interaction mechanism. Firstly, we transformed the linguistic preferences into clouds and used cloud similarity to divide large-scale decision makers into several groups. Secondly, an improved PageRank algorithm based on the trust relationship was developed to calculate the weights of decision makers. A combined weighting method considering the similarity and group size was also presented to calculate the weights of groups. Thirdly, a trust updating mechanism based on cloud similarity, consensus level, and cooperation willingness was developed to speed up the consensus-reaching process, and an opinion interaction mechanism was constructed to measure the consensus level of decision makers. Finally, a numerical experiment effectively illustrated the feasibility of the proposed method. The proposed method was proven to maximally retain the randomness and fuzziness of the decision information during a consensus-reaching process with fast convergent speed and good practicality. |
| first_indexed | 2024-03-09T16:08:31Z |
| format | Article |
| id | doaj.art-e0590c425ecd4594b1e60d7d469d9e43 |
| institution | Directory Open Access Journal |
| issn | 1300-686X 2297-8747 |
| language | English |
| last_indexed | 2024-03-09T16:08:31Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj.art-e0590c425ecd4594b1e60d7d469d9e432023-11-24T16:30:53ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472022-11-0127610110.3390/mca27060101A Large Group Emergency Decision-Making Method Based on Uncertain Linguistic Cloud Similarity MethodGang Chen0Lihua Wei1Jiangyue Fu2Chengjiang Li3Gang Zhao4School of Management, Guizhou University, Guiyang 550025, ChinaSchool of Business, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Management, Guizhou University, Guiyang 550025, ChinaSchool of Management, Guizhou University, Guiyang 550025, ChinaSchool of Engineering, University of Tasmania, Hobart, TAS 7005, AustraliaIn recent years, the consensus-reaching process of large group decision making has attracted much attention in the research society, especially in emergency environment area. However, the decision information is always limited and inaccurate. The trust relationship among decision makers has been proven to exert important impacts on group consensus. In this study, we proposed a novel uncertain linguistic cloud similarity method based on trust update and the opinion interaction mechanism. Firstly, we transformed the linguistic preferences into clouds and used cloud similarity to divide large-scale decision makers into several groups. Secondly, an improved PageRank algorithm based on the trust relationship was developed to calculate the weights of decision makers. A combined weighting method considering the similarity and group size was also presented to calculate the weights of groups. Thirdly, a trust updating mechanism based on cloud similarity, consensus level, and cooperation willingness was developed to speed up the consensus-reaching process, and an opinion interaction mechanism was constructed to measure the consensus level of decision makers. Finally, a numerical experiment effectively illustrated the feasibility of the proposed method. The proposed method was proven to maximally retain the randomness and fuzziness of the decision information during a consensus-reaching process with fast convergent speed and good practicality.https://www.mdpi.com/2297-8747/27/6/101uncertain linguistic informationclouds modeltrust updateconsensus levelclustering algorithm |
| spellingShingle | Gang Chen Lihua Wei Jiangyue Fu Chengjiang Li Gang Zhao A Large Group Emergency Decision-Making Method Based on Uncertain Linguistic Cloud Similarity Method Mathematical and Computational Applications uncertain linguistic information clouds model trust update consensus level clustering algorithm |
| title | A Large Group Emergency Decision-Making Method Based on Uncertain Linguistic Cloud Similarity Method |
| title_full | A Large Group Emergency Decision-Making Method Based on Uncertain Linguistic Cloud Similarity Method |
| title_fullStr | A Large Group Emergency Decision-Making Method Based on Uncertain Linguistic Cloud Similarity Method |
| title_full_unstemmed | A Large Group Emergency Decision-Making Method Based on Uncertain Linguistic Cloud Similarity Method |
| title_short | A Large Group Emergency Decision-Making Method Based on Uncertain Linguistic Cloud Similarity Method |
| title_sort | large group emergency decision making method based on uncertain linguistic cloud similarity method |
| topic | uncertain linguistic information clouds model trust update consensus level clustering algorithm |
| url | https://www.mdpi.com/2297-8747/27/6/101 |
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