Sustainable Decision Making Using a Consensus Model for Consistent Hesitant Fuzzy Preference Relations - Water Allocation Management Case Study

被引:2
|
作者
Rehman, Atiq-ur [1 ]
Watrobski, Jaroslaw [2 ]
Faizi, Shahzad [3 ]
Rashid, Tabasam [4 ]
Tarczynska-Luniewska, Malgorzata [5 ]
机构
[1] COMSATS Univ Islamabad Lahore, Dept Math, Lahore 5400, Pakistan
[2] Univ Szczecin, Inst Management, Cukrowa 6, PL-71104 Szczecin, Poland
[3] Virtual Univ Pakistan, Dept Math, Lahore 54000, Pakistan
[4] Univ Management & Technol, Dept Math, Lahore 54770, Pakistan
[5] Univ Szczecin, Inst Econ & Finance, Mickiewicza 64, PL-71101 Szczecin, Poland
来源
SYMMETRY-BASEL | 2020年 / 12卷 / 12期
关键词
consistency weights; fuzzy preference relation (FPR); hesitant fuzzy preference relation (HFPR); Lukasiewicz consistency; normal hesitant fuzzy preference relation (NHFPR); ADDITIVE CONSISTENCY; BEST-WORST; INFORMATION; FRAMEWORK; MINIMUM; COST; GDM;
D O I
10.3390/sym12121957
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
This paper presents an improved consensus-based procedure to handle multi-person decision making (MPDM) using hesitant fuzzy preference relations (HFPRs) which are not in normal format. At the first level, we proposed a ukasiewicz transitivity (TL-transitivity) based scheme to get normalized hesitant fuzzy preference relations (NHFPRs), subject to which, a consensus-based model is established. Then, a transitive closure formula is defined to construct TL-consistent HFPRs and creates symmetrical matrices. Following this, consistency analysis is made to estimate the consistency degrees of the information provided by the decision-makers (DMs), and consequently, to assign the consistency weights to them. The final priority weights vector of DMs is calculated after the combination of consistency weights and predefined priority weights (if any). The consensus process concludes whether the aggregation of data and selection of the best alternative should be originated or not. The enhancement mechanism is indulged in improving the consensus measure among the DMs, after introducing an identifier used to locate the weak positions, in case of the poor consensus reached. In the end, a comparative example reflects the applicability and the efficiency of proposed scheme. The results show that the proposed method can offer useful comprehension into the MPDM process.
引用
收藏
页码:1 / 19
页数:19
相关论文
共 50 条
  • [21] A consensus model for group decision making with incomplete fuzzy preference relations
    Herrera-Viedma, Enrique
    Alonso, Sergio
    Chiclana, Francisco
    Herrera, Francisco
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2007, 15 (05) : 863 - 877
  • [22] Decision framework of group consensus with hesitant fuzzy linguistic preference relations
    Lin, Yang
    Wang, Ying-Ming
    CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 2020, 5 (03) : 157 - 164
  • [23] Consistency and consensus checking and improving methods for group decision-making with hesitant fuzzy preference relations
    Li, Jian
    Niu, Li-li
    Chen, Qiongxia
    Li, Mei
    JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2024,
  • [24] Group decision making based on acceptable multiplicative consistency and consensus of hesitant fuzzy linguistic preference relations
    Zhang, Zhiming
    Chen, Shyi-Ming
    INFORMATION SCIENCES, 2020, 541 : 531 - 550
  • [25] Consensus reaching with consistency control in group decision making with incomplete hesitant fuzzy linguistic preference relations
    Li, Zhuolin
    Zhang, Zhen
    Yu, Wenyu
    COMPUTERS & INDUSTRIAL ENGINEERING, 2022, 170
  • [26] A consistency and consensus-based method for group decision making with hesitant fuzzy linguistic preference relations
    Zhang, Zhiming
    Chen, Shyi-Ming
    INFORMATION SCIENCES, 2019, 501 : 317 - 336
  • [27] A survey of decision making with hesitant fuzzy preference relations: Progress and prospect
    Xu Z.
    Ren P.
    Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice, 2020, 40 (08): : 2193 - 2202
  • [28] Group decision making with interval linguistic hesitant fuzzy preference relations
    Tang, Jie
    Meng, Fanyong
    Zhang, Shaolin
    An, Qingxian
    EXPERT SYSTEMS WITH APPLICATIONS, 2019, 119 : 231 - 246
  • [29] Multi-stage optimization model for hesitant qualitative decision making with hesitant fuzzy linguistic preference relations
    Peng Wu
    Ligang Zhou
    Huayou Chen
    Zhifu Tao
    Applied Intelligence, 2020, 50 : 222 - 240
  • [30] Multi-stage optimization model for hesitant qualitative decision making with hesitant fuzzy linguistic preference relations
    Wu, Peng
    Zhou, Ligang
    Chen, Huayou
    Tao, Zhifu
    APPLIED INTELLIGENCE, 2020, 50 (01) : 222 - 240