Multi-criteria Group Decision-making Method Based on Expert Trust Network and Cloud Model

2021 ◽  
Author(s):  
Xin Luo ◽  
Huajun Zhang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Cloud Model ◽  
Trust Network

An Uncertain Linguistic Multi-criteria Group Decision-Making Method Based on a Cloud Model

2014 ◽  
Vol 24 (1) ◽  
pp. 171-192 ◽  
Author(s):  
Jian-qiang Wang ◽  
Juan-juan Peng ◽  
Hong-yu Zhang ◽  
Tao Liu ◽  
Xiao-hong Chen
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Cloud Model

Similarity–trust network for clustering‐based consensus group decision‐making model

2021 ◽  
Author(s):  
Wan Syahimi Afiq Wan Ahlim ◽  
Nor Hanimah Kamis ◽  
Sharifah Aniza Sayed Ahmad ◽  
Francisco Chiclana
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Trust Network ◽  
Consensus Group ◽  
Decision Making Model

scholarly journals RISKY MULTI-CRITERIA GROUP DECISION MAKING ON GREEN CAPACITY INVESTMENT PROJECTS BASED ON SUPPLY CHAIN

2017 ◽  
Vol 18 (3) ◽  
pp. 355-372 ◽  
Author(s):  
Yan SONG ◽  
Shuang YAO ◽  
Donghua YU ◽  
Yan SHEN
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Group Decision Making ◽  
Group Decision ◽  
Cloud Model ◽  
Decision Makers ◽  
Capacity Investment ◽  
Decision Matrix ◽  
Investment Projects ◽  
Green Capacity

Green capacity investment projects have rapidly emerged involving suppliers, customers, and manufacturing organizations in supply chain systems with environmental challenges. This paper focuses on and identifies both primary strategic and operational elements that will aid managers in evaluating and making risky multi-criteria decisions on green capacity investment projects. We propose a cloud prospect value consensus process consisting of feedback and adjustment mechanisms that provide modification instructions to the corresponding decision makers for a decision matrix based on the cloud model and prospect theory, which considers psychological behavior, disagreements between decision makers, and the ambiguity of linguistic variable assessment across multi-criteria risks. The new model increases the efficiency and accuracy of decision making. To verify the feasibility and validity of the Cloud Prospect Value Consensus Degree based on the Feedback adjustment mechanism, its performance is compared with three state-of-the-art multi-criteria group decision-making methods.

A self-testing cloud model for multi-criteria group decision making

2016 ◽  
Vol 33 (6) ◽  
pp. 1767-1783 ◽  
Author(s):  
Ting-Cheng Chang ◽  
Hui Wang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Cloud Model ◽  
New Approach ◽  
Content Type ◽  
Qualitative And Quantitative ◽  
Composite Scale ◽  
Self Testing

Purpose – The purpose of this paper is to select the best scaling coefficient during the quantitative-qualitative conversion. Design/methodology/approach – Cloud model can describe the qualitative concept of randomness and fuzziness, achieve uncertain transition between qualitative and quantitative in the field of multi-criteria group decision and has been receiving widespread attention. This paper discusses scale conversion issues of the cloud model when evaluating qualitative information. In order to improve the accuracy of the evaluation on multi-attribute decision problems based on uncertainty of natural linguistic information, this paper proposes a method of self-testing cloud model based on a composite scale (with the exponential scale and the scale as a basis). Findings – Through experimental verification results show that under composite scale, the best suitable selection of can effectively improve the accuracy and reliability of decision results. Originality/value – This research presents a new approach to determine the suitable value for coefficient based on uncertain knowledge of natural multi-criteria group decision making, and gives concrete steps and examples. This method has positive significance to improve the quality of qualitative and quantitative conversion based on cloud model.

Risky Group Decision-Making Method for Distribution Grid Planning

2015 ◽  
Vol 16 (6) ◽  
pp. 591-602 ◽  
Author(s):  
Cunbin Li ◽  
Jiahang Yuan ◽  
Zhiqiang Qi
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Cloud Model ◽  
Orthogonal Experiment ◽  
Distribution Grid ◽  
Validity And Reliability ◽  
Interval Numbers ◽  
Decision Vector ◽  
Grid Planning

Abstract With rapid speed on electricity using and increasing in renewable energy, more and more research pay attention on distribution grid planning. For the drawbacks of existing research, this paper proposes a new risky group decision-making method for distribution grid planning. Firstly, a mixing index system with qualitative and quantitative indices is built. On the basis of considering the fuzziness of language evaluation, choose cloud model to realize “quantitative to qualitative” transformation and construct interval numbers decision matrices according to the “3En” principle. An m-dimensional interval numbers decision vector is regarded as super cuboids in m-dimensional attributes space, using two-level orthogonal experiment to arrange points uniformly and dispersedly. The numbers of points are assured by testing numbers of two-level orthogonal arrays and these points compose of distribution points set to stand for decision-making project. In order to eliminate the influence of correlation among indices, Mahalanobis distance is used to calculate the distance from each solutions to others which means that dynamic solutions are viewed as the reference. Secondly, due to the decision-maker’s attitude can affect the results, this paper defines the prospect value function based on SNR which is from Mahalanobis-Taguchi system and attains the comprehensive prospect value of each program as well as the order. At last, the validity and reliability of this method is illustrated by examples which prove the method is more valuable and superiority than the other.

scholarly journals A Method of Capturing and Transferring Customer Value in Product Service System

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Hua Ding ◽  
Hengqiang Liu ◽  
Kun Yang
Keyword(s):  
Decision Making ◽  
Correction Factor ◽  
Group Decision Making ◽  
Hierarchical Model ◽  
Customer Value ◽  
Group Decision ◽  
Service System ◽  
Cloud Model ◽  
Product Service System ◽  
Product Service

The methods of capturing and transferring the customer value in a product service system (PSS) are studied to capture the customers’ intrinsic value requirements, grasp the importance level of requirement, and transform it into design elements to more reasonably allocate resources and develop products more in line with the customers’ needs and more competitive at a minimum cost. First, a hierarchical model of the customer value based on the means-end chain theory is constructed to analyze the customer value from the perspective of customer expectations. In the process of determining the importance priority of value elements, the cloud model is used to process the expert evaluation information, and the competitive correction factor and the Kano factor are used to modify the basic importance of the value elements. The customer value in the PSS is then transferred to the product and service performance domain by constructing the parallel house of quality embedded cloud model (PHOQ-ECM). In other words, the cloud model is used to process the group decision-making values with fuzziness and randomness to complete the correlation calculation of the parallel HOQ. The important priority of the performance characteristics is then obtained. Finally, the abovementioned methods are applied to capture and transfer the customer value of a shearer, and the results are compared with other studies. The results show that the hierarchical model of the customer value can more deeply capture the customer value. The cloud model solves the problem of group decision-making with fuzziness and randomness. The competition correction factor and the Kano factor improve the accuracy of the importance priority of the value elements. PHOQ-ECM achieves the transfer and distribution of the customer value to two different objects of product and service and improves the accuracy of the performance characteristics importance priority. The method feasibility and validity are verified through the abovementioned analysis. Consequently, the method can effectively guide the PSS design.

CLOUD DELPHI METHOD

2012 ◽  
Vol 20 (01) ◽  
pp. 77-97 ◽  
Author(s):  
XIAO-JUN YANG ◽  
LUAN ZENG ◽  
RAN ZHANG
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Group Decision Making ◽  
Delphi Method ◽  
Group Decision ◽  
Cloud Model ◽  
Weighted Average ◽  
Implementation Process ◽  
Numerical Example ◽  
The Individual

Group decision making is an important category of problem solving techniques for complicated problems, among which the Delphi method has been widely applied. In this paper an improved Delphi method based on Cloud model is proposed in order to deal with the fuzziness and uncertainty in experts' subjective judgments. The proposed Cloud Delphi Method (CDM) describes experts' opinions by Cloud model and we aggregate the experts' Cloud opinions by synthetic algorithm and weighted average algorithm. Another key point of CDM is to stabilize and accommodate the individual fuzzy estimates by the defined stability rules rather than having to force them to converge, or reduce. The Cloud opinions and aggregation results can be exhibited in a graphically way leading experts to judge intuitively and it can decrease the number of repetitive surveys and/or interviews. Moreover, it is more scientific and easier to represent experts' opinion base on Cloud model which can combine fuzziness and uncertainty well. A numerical example is examined to demonstrate applicability and implementation process of CDM.

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