TODIM method for probabilistic linguistic multiple attribute group decision making based on the similarity measures and entropy

被引:8
|
作者
Wei, Cun [1 ]
Wu, Jiang [1 ]
机构
[1] Southwestern Univ Finance & Econ, Sch Stat, Chengdu 611130, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
Multiple attribute group decision making (MAGDM); probabilistic linguistic term sets (PLTSs); TODIM method; Prospect theory; green supplier selection; INTUITIONISTIC FUZZY; TERM SETS; AGGREGATION OPERATORS; SUPPLIER SELECTION; 2-TUPLE; MODELS; DESTINATION; MANAGEMENT; APPRAISAL; DISTANCE;
D O I
10.3233/JIFS-191164
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recently, the TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) method, which can characterize the decision makers' psychological behaviors under risk, has been introduced to handle multiple attribute group decision making (MAGDM) problems. Moreover, the probabilistic linguistic term sets (PLTSs) are effective tool for depicting uncertainty of the MAGDM problems. In this paper, we extend the TODIM method to the MAGDM with PLTSs. Firstly, the definition, comparative method and distance of PLTSs are simply introduced, and the steps of the classical TODIM method for MAGDM problems are presented. Then, on the basis of the conventional TODIM method, the extended TODIM method is proposed to deal with MAGDM problems in which the attribute values are depicted in the PLTSs, and its significant characteristic is that it can fully consider the decision makers' bounded rationality which is a real action in decision making. Finally, a numerical example for green supplier selection is proposed to verify the developed approach and its practicality and effectiveness.
引用
收藏
页码:7025 / 7037
页数:13
相关论文
共 50 条
  • [1] Probabilistic linguistic TODIM approach for multiple attribute decision-making
    Liu P.
    You X.
    [J]. Granular Computing, 2017, 2 (4) : 333 - 342
  • [2] Probabilistic linguistic GRA method for multiple attribute group decision making
    Wei, Guiwu
    Lu, Jianping
    Wei, Cun
    Wu, Jiang
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 38 (04) : 4721 - 4732
  • [3] TODIM method based on the CRITIC method for multi-attribute group decision making with dual probabilistic linguistic information
    Wang, Zeyuan
    Wei, Guiwu
    Guo, Yanfeng
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2022, 43 (06) : 7261 - 7276
  • [4] An extended TODIM method for multiple attribute group decision making based on intuitionistic uncertain linguistic variables
    Liu, Peide
    Teng, Fei
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2015, 29 (02) : 701 - 711
  • [5] Extension of the TODIM Method to Intuitionistic Linguistic Multiple Attribute Decision Making
    Wang, Shuwei
    Liu, Jia
    [J]. SYMMETRY-BASEL, 2017, 9 (06):
  • [6] Similarity Measures of Linguistic Cubic Hesitant Variables for Multiple Attribute Group Decision-Making
    Lu, Xueping
    Ye, Jun
    [J]. INFORMATION, 2019, 10 (05)
  • [7] A projection method for multiple attribute group decision making with probabilistic linguistic term sets
    Xiaofang Zhang
    Xunjie Gou
    Zeshui Xu
    Huchang Liao
    [J]. International Journal of Machine Learning and Cybernetics, 2019, 10 : 2515 - 2528
  • [8] A projection method for multiple attribute group decision making with probabilistic linguistic term sets
    Zhang, Xiaofang
    Gou, Xunjie
    Xu, Zeshui
    Liao, Huchang
    [J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2019, 10 (09) : 2515 - 2528
  • [9] MABAC method for multiple attribute group decision making with probabilistic uncertain linguistic information
    Wei, Guiwu
    He, Yan
    Lei, Fan
    Wu, Jiang
    Wei, Cun
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 39 (03) : 3315 - 3327
  • [10] Probabilistic linguistic multiple attribute group decision making for location planning of electric vehicle charging stations based on the generalized Dice similarity measures
    Wei, Guiwu
    Wei, Cun
    Wu, Jiang
    Guo, Yanfeng
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2021, 54 (06) : 4137 - 4167