[1]金薇,钱进,余鹰,等.基于多粒度犹豫模糊语言术语集的TOPSIS决策方法研究[J].智能系统学报,2024,19(4):1052-1060.[doi:10.11992/tis.202306015]
JIN Wei,QIAN Jin,YU Ying,et al.Research on TOPSIS decision-making method based on multi-granularity hesitant fuzzy linguistic term sets[J].CAAI Transactions on Intelligent Systems,2024,19(4):1052-1060.[doi:10.11992/tis.202306015]
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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第4期
页码:
1052-1060
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2024-07-05
- Title:
-
Research on TOPSIS decision-making method based on multi-granularity hesitant fuzzy linguistic term sets
- 作者:
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金薇1, 钱进1, 余鹰1, 苗夺谦1,2
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1. 华东交通大学 软件学院, 江西 南昌 330013;
2. 同济大学 电子与信息工程学院, 上海 201804
- Author(s):
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JIN Wei1, QIAN Jin1, YU Ying1, MIAO Duoqian1,2
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1. School of Software, East China Jiaotong University, Nanchang 330013, China;
2. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
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- 关键词:
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多粒度; 多属性决策; 犹豫模糊集; 语言术语集; 模糊语言; 决策模型; 逼近理想解排序法; 最优方案选择
- Keywords:
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multi-granularity; multi-attribute decision; hesitant and fuzzy set; linguistic term set; ambiguous linguistic; decision model; TOPSIS method; optimal solution selection
- 分类号:
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TP391
- DOI:
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10.11992/tis.202306015
- 摘要:
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为了解决在实际决策时,由于知识背景不同决策者采用不同粒度语言术语集来表达而导致决策结果不准确的问题,本文提出了一种基于多粒度犹豫模糊语言术语集的逼近理想解排序(technique for order preference by similarity to ideal solution, TOPSIS)决策方法。首先选用各术语集中的最大粒度作为标准粒度,通过转换算法将每个决策者的语言术语集转换到同一标准粒度下进行集结,得出相应的隶属度语言术语集;然后结合TOPSIS方法,计算每个备选方案与正、负理想点距离,以相对贴近度的大小排序实现最优方案的选择;最后,通过一个实例,验证该方法的可行性和优越性。本文所提方法可应用于最优方案的选择问题中,提升决策结果准确度。
- Abstract:
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In order to solve the problem that decision makers adopt different granularity linguistic term sets for expression and thus lead to inaccurate decision results due to different knowledge backgrounds in practical decision making, this paper proposes a technique for order preference by similarity to ideal solution(TOPSIS) decision-making method based on multi-granularity hesitant fuzzy linguistic term sets. Firstly, the maximum granularity of each term set is selected as the standard granularity, and the linguistic term set of each decision maker is converted to the same standard granularity for clustering through the conversion algorithm, which results in corresponding subordination linguistic term set; Then, combining with TOPSIS, the distance between each alternative and the positive and negative ideal points is calculated, and the selection of the optimal solution is realized by the ordering of the magnitude of relative closeness; Finally, the feasibility and superiority of the method are verified by an example. The method proposed in this paper can be applied to the problem of choosing the optimal solution to improve the accuracy of decision-making results.
备注/Memo
收稿日期:2023-06-08。
基金项目:国家自然科学基金项目(62066014,62163016,61976158);江西省自然科学基金项目(20202BAB202010,20212ACB202001).
作者简介:金薇,硕士研究生,主要研究方向为粒计算、模糊集、多属性决策。E-mail:jinwei_1009@163.com;钱进,教授,博士,主要研究方向为粒计算、大数据挖掘和机器学习。主持国家自然科学基金项目2项、省部级自然科学基金项目3项,获江西省自然科学奖1项。发表学术论文50余篇。E-mail:qjqjlqyf@163.com;苗夺谦,教授,主要研究方向为粒计算、不确定性、大数据分析。国际粗糙集学会理事长、中国人工智能学会会士、中国计算机学会杰出会员。发表学术论文180余篇。E-mail:miaoduoqian@163.com
通讯作者:钱进. E-mail:qjqjlqyf@163.com
更新日期/Last Update:
1900-01-01