Multiplicative Consistency Ascertaining, Inconsistency Repairing, and Weights Derivation of Hesitant Multiplicative Preference Relations

被引:22
|
作者
Xu, Yejun [1 ]
Li, Mengqi [2 ]
Chiclana, Francisco [3 ,4 ]
Herrera-Viedma, Enrique [3 ,5 ]
机构
[1] Tianjin Univ, Coll Management & Econ, Tianjin 300072, Peoples R China
[2] Hohai Univ, Business Sch, Nanjing 211100, Peoples R China
[3] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada 18071, Spain
[4] De Montfort Univ, Inst Artificial Intelligence, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics, England
[5] King Abdulaziz Univ, Dept Elect & Comp Engn, Fac Engn, Jeddah 21589, Saudi Arabia
基金
中国国家自然科学基金;
关键词
Consistency ascertaining; hesitant multiplicative preference relations (HMPRs); inconsistency repairing; missing values; weights derivation; GROUP DECISION-MAKING; CONSENSUS-REACHING PROCESS; PRIORITY WEIGHTS; ADDITIVE CONSISTENCY; MISSING VALUES; FUZZY; MODEL; PRIORITIZATION; INFORMATION; FUSION;
D O I
10.1109/TSMC.2021.3099862
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article investigates multiplicative consistency ascertaining, inconsistency repairing, and weights derivation for hesitant multiplicative preference relations (HMPRs). First, the completely multiplicative consistency and weakly multiplicative consistency of HMPRs are defined. Based on them, 0-1 mixed programming models and simple algebraic operations are proposed to ascertain the multiplicative consistency of HMPRs. Then, some goal programming models are developed to generate the weights from consistent HMPRs and to revise inconsistent HMPRs. An integrated procedure to manage the multiplicative consistencies of HMPRs is designed. The proposed methods are also extended to accommodate incomplete HMPRs, and to estimate missing values. Finally, some numerical examples, a comparative analysis with existent approaches, and a simulation analysis are included to illustrate the practicality and effectiveness of the developed models.
引用
收藏
页码:6806 / 6821
页数:16
相关论文
共 50 条
  • [1] On the use of multiplicative consistency in hesitant fuzzy linguistic preference relations
    Zhang, Zhiming
    Wu, Chong
    [J]. KNOWLEDGE-BASED SYSTEMS, 2014, 72 : 13 - 27
  • [2] Deriving the priority weights from incomplete hesitant fuzzy preference relations based on multiplicative consistency
    Zhang, Zhiming
    [J]. APPLIED SOFT COMPUTING, 2016, 46 : 37 - 59
  • [3] Consensus building for hesitant fuzzy preference relations with multiplicative consistency
    Li, Jian
    Wang, Jian-qiang
    Hu, Jun-hua
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2019, 128 : 387 - 400
  • [4] Improving the additive and multiplicative consistency of hesitant fuzzy linguistic preference relations
    Liu, Hongbin
    Jiang, Le
    Xu, Zeshui
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2017, 33 (06) : 3677 - 3693
  • [5] A new procedure for hesitant multiplicative preference relations
    Meng, Fanyong
    Tang, Jie
    An, Qingxian
    Chen, Xiaohong
    [J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2019, 34 (05) : 819 - 857
  • [6] The Multiplicative Consistency Index of Hesitant Fuzzy Preference Relation
    Liu, Haifeng
    Xu, Zeshui
    Liao, Huchang
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2016, 24 (01) : 82 - 93
  • [7] Improving the consistency of incomplete hesitant multiplicative preference relation
    Mamata Sahu
    Anjana Gupta
    [J]. OPSEARCH, 2019, 56 : 324 - 343
  • [8] Improving the consistency of incomplete hesitant multiplicative preference relation
    Sahu, Mamata
    Gupta, Anjana
    [J]. OPSEARCH, 2019, 56 (01) : 324 - 343
  • [9] Consistency analysis and priority weights of multiplicative trapezoidal fuzzy preference relations based on multiplicative consistency and logarithmic least square model
    Wu, Peng
    Li, Hongyan
    Zhou, Ligang
    Chen, Huayou
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2019, 37 (06) : 8317 - 8334
  • [10] Deriving the priority weights from hesitant multiplicative preference relations in group decision making
    Zhang, Zhiming
    Wu, Chong
    [J]. APPLIED SOFT COMPUTING, 2014, 25 : 107 - 117