| 基于余弦贴近度与群体共识度的正态云多准则群决策方法 |
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| 引用本文： | 任剑,王坚强,胡春华. 基于余弦贴近度与群体共识度的正态云多准则群决策方法[J]. 控制与决策, 2017, 32(4): 665-672 |
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| 作者姓名： | 任剑 王坚强 胡春华 |
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| 作者单位： | 湖南商学院湖南省移动电子商务协同创新 中心,长沙410205;湖南大学工商管理学院,长沙410082,中南大学商学院,长沙410083,湖南商学院湖南省移动电子商务协同创新 中心,长沙410205 |
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| 基金项目： | 国家社会科学基金一般项目(15BJY163);教育部人文社会科学研究青年基金项目(13YJCZH145);国家自然科学基金重点项目(71431006);国家自然科学基金面上项目(61273232);中国博士后科学基金面上项目(2013M531784);教育部新世纪优秀人才支持计划项目(NCET-13-0785);湖南省教育厅科学研究优秀青年项目(15B129). |
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| 摘 要： | 对于正态云多准则群决策问题,提出一种基于余弦贴近度与群体共识度的决策方法. 将各专家的正态云决策矩阵转化为泛型正态随机决策矩阵, 利用正态云的线性组合方法, 计算出线性加权泛型正态随机变量. 基于理想方案, 求得各专家认为各方案的余弦相似度、余弦贴近度和排序结果. 通过群体共识度, 判断专家们决策意见的一致性程度, 求得各方案的加权的余弦相似度、余弦贴近度, 并得到最终排序. 最后, 通过算例验证了所提出方法的可行性和有效性.
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| 关 键 词： | 多准则群决策 正态云 泛型正态随机变量 理想方案 余弦相似度 余弦贴近度 群体共识度 |
Multi-criterion group decision-making method based on normal cloud by cosine close degree and group consensus degree |
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| REN Jian,WANG Jian-qiang and HU Chun-hua. Multi-criterion group decision-making method based on normal cloud by cosine close degree and group consensus degree[J]. Control and Decision, 2017, 32(4): 665-672 |
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| Authors: | REN Jian WANG Jian-qiang HU Chun-hua |
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| Affiliation: | Mobile E-business Collaborative Innovation Center of Hu''nan Province,Hu''nan University of Commerce, Changsha410205,China;School of Business,Hu''nan University,Changsha410082,China,School of Business,Central South University,Changsha410083,China and Mobile E-business Collaborative Innovation Center of Hu''nan Province,Hu''nan University of Commerce, Changsha410205,China |
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| Abstract: | For the multi-criterion group decision-making problems with normal clouds under the criterion set, a decision-making method based on cosine close degrees and group consensus degrees is proposed. In the method, firstly, the normal cloud decision-making matrices of the experts are transformed into general normal stochastic decision-making matrices. Then, by using the linear combination method of normal clouds, the linear weighted general normal stochastic variables of the alternatives are derived. Furthermore, based on ideal alternatives, the cosine similarity degrees, cosine close degrees and ranking results are attained by each expert. After that, through the group consensus degree, the consistency degree of the decision-making opinions of the expert sets is worked out. Accordingly, the weighted cosine similarity degree, weighted cosine close degree and the comprehensive ranking order are gained. Finally, the feasibility and effectiveness of the proposed method are verified by the comparative analysis of an illustrative example. |
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