A multi-criterion group decision-making method based on regret theory under 2-tuple linguistic environment

Jia, Xu; Wang, Ying‐Ming

doi:10.1108/k-08-2021-0757

Cited by 7 publications

(3 citation statements)

References 52 publications

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“…Compared with q-RPDHFSs, the newly developed q-RIVPDHFSs allow interval-valued probabilistic information, which makes them more powerful and useful to handle decision makers' In future works, we plan to continue our studies from two aspects. First, we shall investigate more applications of our methods in more down-to-earth MAGDM problems, such as supplier selection [53,54], investment projection selection [55,56], best airline selection [57,58], low-carbon supplier selection [59,60], and healthcare waste treatment technology selection [61,62]. Second, we shall study more aggregation operators for fusing q-rung interval-valued probabilistic dual hesitant fuzzy information.…”

Section: Discussionmentioning

confidence: 99%

Q‐Rung Interval‐Valued Probabilistic Dual Hesitant Fuzzy Sets: A New Tool for Multiattribute Group Decision‐Making

Zhang

Wang

2023

Mathematical Problems in Engineering

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This paper aims at proposing a novel multiattribute group decision-making (MAGDM) method in complex decision-making environments. To this end, we first introduce a tool, called q-rung interval-valued probabilistic dual hesitant fuzzy sets (q-RIVPDHFSs), for decision makers to express their evaluation information over a set of finite alternatives in MAGDM procedures. The q-RIVPDHFS consists of some possible membership and nonmembership degrees, along with their interval-valued probabilistic information. Due to this structure, q-RIVPDHFSs are more powerful and flexible than the traditional q-rung probabilistic q-rung dual hesitant fuzzy sets, in which probabilistic information of membership and nonmembership degree is denoted by crisp numbers. Second, some other related concepts of q-RIVPDHFSs, such as operational laws, comparison method, distance measure, and aggregation operators, are introduced. Third, based on these novel concepts, two MAGDM methods (Algorithms 1 and 2) are put forward. Last but not least, a practical decision-making example is provided to show the effectiveness of our proposed MAGDM method. We also compare our Algorithms 1 and 2 with some existing decision-making methods to explain why our methods are more powerful and useful.

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Section: Discussionmentioning

confidence: 99%

Q‐Rung Interval‐Valued Probabilistic Dual Hesitant Fuzzy Sets: A New Tool for Multiattribute Group Decision‐Making

Zhang

Wang

2023

Mathematical Problems in Engineering

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“…However, due to the complexity of social environment and the limitation of human cognition, for practical TSMDM problems of CMfg, hesitation and fuzziness always exist in the process of multi-criteria information assessment. Under such a situation, qualitative linguistic terms and interval fuzzy numbers are more suitable for expressing satisfaction indicators than precise quantitative numbers (Jia and Wang, 2021). Consequently, a number of linguistic decision-making methods emerged for assessing multi-criteria preference information, and these methods have been applied widely to various fields.…”

Section: Introductionmentioning

confidence: 99%

Workload balance-based dynamic two-sided matching decision-making approach for cloud manufacturing tasks and services under uncertain preferences

Zhang

Yang²,

Wang³

2022

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PurposeThis paper aims to develop a dynamic two-sided stable matching method based on preference information of the matching objects in uncertain environments, so as to solve the matching problem of cloud manufacturing tasks and services with load balancing.Design/methodology/approachFor dynamic two-sided matching, due to the complexity of social environment and the limitation of human cognition, hesitation and fuzziness always exist in the process of multi-criteria assessment. First, in order to obtain the accurate preference information of each matching object, uncertain linguistic variables, uncertain preference ordinal and incomplete complementary matrices are used to evaluate multi-criteria preference information. This process is undertaken by considering the probability of each possible matching pair. Second, the preference information at different times is integrated by using the time-series weight to obtain the comprehensive satisfaction degree matrices of the matching objects. Further, the load adjustment parameter is used to increase the satisfaction degree of the matching objects. Afterward, a dynamic two-sided stable matching optimization model is constructed by considering stable matching conditions. The model aims to maximize the satisfaction degree and minimizes the difference in the satisfaction degree of matching objects. The optimal stable matching results can be obtained by solving the optimization model. Finally, a numerical example and comparative analysis are presented to demonstrate the characteristics of the proposed method.FindingsUncertain linguistic variables, uncertain preference orders and incomplete complementary matrices are used to describe multi-criteria preference information of the matching objects in uncertain environments. A dynamic two-sided stable matching method is proposed, based on which a DTSMDM (dynamic two-sided matching decision-making) model of cloud manufacturing with load balancing can be constructed. The study proved that the authors can use the proposed method to obtain stable matching pairs and higher matching objective value through comparative analysis and the sensitivity analysis.Originality/valueA new method for the two-sided matching decision-making problem of cloud manufacturing with load balancing is proposed in this paper, which allows the matching objects to elicit language evaluation under uncertain environment more flexibly to implement dynamic two-sided matching based on preference information at different times. This method is suitable for dealing with a variety of TSMDM (two-sided matching decision-making) problems.

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“…In the CRP, to improve the collective consensus degree, several experts are required to adjust their evaluations to make them more similar or closer (Dong et al, 2018). To this end, many CRP models (Garcı et al, 2012;Wu et al, 2017Wu et al, , 2018Liu et al, 2021a;Jia and Wang, 2021;Zhang et al, 2019Zhang et al, , 2020bCheng et al, 2020Cheng et al, , 2022Qin and Liu, 2016;Du et al, 2021;Xiao et al, 2022;Zha et al, 2019;Sun et al, 2021) are proposed to guide experts in modifying their evaluations. Based on the Stackelberg game, Zhang et al (2020b) designed a consensus model from the perspectives of both the moderator and experts.…”

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confidence: 99%

A dynamic trust consensus model based on personalized adjustment mechanism under multi-granular probabilistic linguistic environment

Guo

Liang

2022

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PurposeThis study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.Design/methodology/approachThe trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.FindingsThe results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.Originality/valueThe authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.

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A multi-criterion group decision-making method based on regret theory under 2-tuple linguistic environment

Cited by 7 publications

References 52 publications

Q‐Rung Interval‐Valued Probabilistic Dual Hesitant Fuzzy Sets: A New Tool for Multiattribute Group Decision‐Making

Q‐Rung Interval‐Valued Probabilistic Dual Hesitant Fuzzy Sets: A New Tool for Multiattribute Group Decision‐Making

Workload balance-based dynamic two-sided matching decision-making approach for cloud manufacturing tasks and services under uncertain preferences

A dynamic trust consensus model based on personalized adjustment mechanism under multi-granular probabilistic linguistic environment

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