Article
Computer Science, Artificial Intelligence
Jian Li, Jianping Ye, Li-li Niu, Qiongxia Chen, Zhong-xing Wang
Summary: This paper develops decision-making models that take into account decision makers' satisfaction degree by defining consistency measures and utilizing additive and multiplicative consistency measures. The objective of these models is to maximize the parameter of satisfaction degree and obtain weights through a quadratic programming model.
Article
Computer Science, Interdisciplinary Applications
Zhuolin Li, Zhen Zhang, Wenyu Yu
Summary: Due to complex decision environment and limited knowledge of decision makers, this paper proposes a method to manage incomplete information and consensus in group decision making. An optimization model is developed to impute missing elements, and feedback adjustment rules are proposed to modify decision makers' preferences. Furthermore, an iterative algorithm is proposed to reach consensus. The feasibility of the algorithm is demonstrated through a numerical example.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Peng Wang, Peide Liu, Francisco Chiclana
Summary: An optimization algorithm is developed in this study for preference decision-making with incomplete probabilistic linguistic preference relation (InPLPR). By constructing a two-stage mathematical optimization model based on expected multiplicative consistency, missing information can be estimated and consistency improved, ultimately leading to the ranking of alternatives.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Monica Matzenauer, Helida Santos, Benjamin Bedregal, Humberto Bustince, Renata Reiser
Summary: This paper discusses consensus measures for typical hesitant fuzzy elements within the scope of typical hesitant fuzzy sets. The approach uses aggregation functions, fuzzy implication-like functions, and fuzzy negations to formally construct consensus measures, with analysis on their consistency. The theoretical results are applied to decision making with multicriteria, showing a methodology to achieve consensus in a group of experts working with typical hesitant fuzzy sets.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Peijia Ren, Xinxin Wang, Zeshui Xu, Xiao-Jun Zeng
Summary: This study proposes a decision-making method that considers individual consistency and group consensus of hesitant fuzzy linguistic preference relations (HFLPRs). It demonstrates the value of this method through experiments and provides support for its application.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Jian Li, Li-li Niu, Qiongxia Chen, Zhong-xing Wang
Summary: This paper develops decision-making models that integrate hesitant fuzzy preference relations with the best worst method, considering various compromise constraints and obtaining decision-makers' comprehensive weights through an absolute programming model. The models efficiently express hesitant information and demonstrate feasibility and efficiency through an illustrative example with comparative analysis.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Peide Liu, Peng Wang, Witold Pedrycz
Summary: The article proposes a method for group decision-making based on incomplete probabilistic linguistic preference relations, considering both consistency and consensus. By classifying InPLTSs specifically, introducing expected multiplicative consistency, and developing a consensus index, the method effectively addresses uncertainty and inconsistency in group decision-making.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Tao Li, Liyuan Zhang, Zhenglong Zhang
Summary: This paper proposes a group decision-making method based on linguistic interval-valued intuitionistic fuzzy preference relations (LIVIFPRs), with a focus on consistency and consensus analysis. The paper introduces the multiplicative consistency of LIVIFPRs and develops a consistency-based model to determine the missing values in incomplete LIVIFPRs. An optimization model is established to repair the inconsistent LIVIFPRs. Linguistic interval-valued intuitionistic fuzzy priority weights are then constructed, and two algorithms are presented for decision-making and group decision-making respectively. The proposed method is applied to evaluate Chinese express companies and its advantages are demonstrated through comparison analysis.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Xiao-Yun Lu, Jiu-Ying Dong, Shu-Ping Wan, Ye -fang Yuan
Summary: In this paper, a goal programming model that incorporates the hesitancy measure and adjustment mechanism is proposed to enhance consistency and consensus. A distance-based aggregation model is then constructed to derive the group multiplicative preference relation (IMPR).
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jian Li, Li-li Niu, Qiongxia Chen, Zhong-xing Wang, Wenjing Li
Summary: This paper proposes a group decision making method considering the additive consistency and consensus, and improves consistency by normalizing HFPRs and designing algorithms. It also provides a method for determining decision makers' weights and a procedure for solving MCDM problems with HFPRs.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Peijia Ren, Zixu Liu, Wei-Guo Zhang, Xilan Wu
Summary: This paper introduces a method for group decision making based on consistency and consensus measurements to handle hesitant fuzzy linguistic preference relation (HFLPR). The effectiveness of the proposed method is demonstrated through experiments, and a specific case is given to illustrate its applicability. An online decision-making portal is also provided for decision makers to utilize the method.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Zhiming Zhang, Witold Pedrycz
Summary: This article investigates group decision-making problems using incomplete interval-valued intuitionistic fuzzy preference relations to represent decision makers' preference information. It proposes an optimization model and defines consistency indices to address these problems, and presents a novel method for group decision-making.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Hyonil Oh, Hyonil Kim, Hyokchol Kim, Cholryong Kim
Summary: This paper proposes a method to improve the multiplicative inconsistency of an intuitionistic fuzzy preference relation (IFPR) without deriving an underlying priority weight's vector. A ratio-based deviation identifying matrix is constructed to accurately measure the deviation of each element in the IFPR. An iterative algorithm is presented to improve the multiplicative consistency based on the deviation matrix. A numerical example is provided to demonstrate the feasibility and efficiency of the proposed method.
INFORMATION SCIENCES
(2022)
Article
Management
Yongming Song, Guangxu Li, Daji Ergu, Na Liu
Summary: This paper proposed a method for group decision making based on PULPRs to manage consistency and consensus, reducing preference information loss through optimization model. By utilizing a conversion function for psychological preferences and a consensus model, a balance between individual consistency and group consensus can be achieved effectively.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2022)
Article
Computer Science, Information Systems
Zhiming Zhang, Shyi-Ming Chen
Summary: This paper proposes a group decision-making method based on Pythagorean fuzzy sets. By introducing the multiplicative consistency and inconsistency-repairing methods, the priority weight vector of PFPRs is derived, and a group consensus index and iterative consensus reaching procedure are proposed. By maximizing the group consensus level, a model is built to determine the weights of DMs. The proposed method outperforms existing methods in Pythagorean fuzzy environments.
INFORMATION SCIENCES
(2022)
