Analysis of Factors Affecting Power Load Characteristics Based on Grey Relational Analysis Model

2019 ◽  
Author(s):  
Jianmin Ding ◽  
Yunli Yue ◽  
Jianhua Chen ◽  
Meng Chen ◽  
Weibo Zhao
Keyword(s):  
Grey Relational Analysis ◽  
Analysis Model ◽  
Factors Affecting ◽  
Relational Analysis ◽  
Power Load ◽  
Load Characteristics ◽  
Grey Relational

Negative grey relational model and measurement of the reverse incentive effect of fields medal

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sifeng Liu
Keyword(s):  
Grey Relational Analysis ◽  
Research Output ◽  
Analysis Model ◽  
Content Type ◽  
Relational Analysis ◽  
Fields Medal ◽  
Definition Of ◽  
Grey Relational ◽  
Analysis Models ◽  
The Relationship

PurposeThe purpose of this paper is to construct some negative grey relational analysis models to measure the relationship between reverse sequences.Design/methodology/approachThe definition of reverse sequence has been given at first based on analysis of relative position and change trend of sequences. Then, several different negative grey relational analysis models, such as the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model have been put forward based on the corresponding common grey relational analysis models. The properties of the new models have been studied.FindingsThe negative grey relational analysis models proposed in this paper can solve the problem of relationship measurement of reverse sequences effectively. All the new negative grey relational degree satisfying the requirements of normalization and reversibility.Practical implicationsThe proposed negative grey relational analysis models can be used to measure the relationship between reverse sequences. As a living example, the reverse incentive effect of winning Fields Medal on the research output of winners is measured based on the research output data of the medalists and the contenders using the proposed negative grey relational analysis model.Originality/valueThe definition of reverse sequence and the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model are first proposed in this paper.

Appliance on Evaluation of Type Selection for Agricultural Machinery Based on Grey Relational Analysis Method

2011 ◽  
Vol 55-57 ◽  
pp. 988-991 ◽  
Author(s):  
Yan Xie ◽  
Mu Li
Keyword(s):  
Grey Relational Analysis ◽  
Analysis Method ◽  
Agricultural Machinery ◽  
Analysis Model ◽  
Relational Analysis ◽  
Type Selection ◽  
The Index System ◽  
Selection For ◽  
Grey Relational ◽  
Optimal Type

The type selection for agricultural machinery generally depends on the selector’s experiences at the present time. It exist some subjectivity and one-sidedness unavoidably. On the basis of the characteristics of complexity and the uncertainty of optimal selection for agricultural machinery, grey relational analysis model in agricultural machinery multi-type selection application has been put forward in this paper. The index system of influencing agricultural machinery type selection is determined. Application model and procedures of the grey relational analysis method are introduced. According to the degree how close it is with the ideal dot, the optimal type is easy to be selected out. Through the optimization analysis type selection for the agricultural transporter, it is proved that the optimal type selection for agricultural machinery based on grey relational analysis has the strong recognition judgment ability. It is convenient, quantitative, and strict. It provided a new method and a possible new way for evaluation of agricultural machinery type selecting．

scholarly journals Factors affecting the effectiveness of quality control circles in a hospital using a combination of fuzzy VIKOR and Grey Relational Analysis

2018 ◽  
Vol 27 (3) ◽  
pp. 180-186 ◽  
Author(s):  
Mohammadkarim Bahadori ◽  
Ehsan Teymourzadeh ◽  
Foad Faizy Bagejan ◽  
Ramin Ravangard ◽  
Mehdi Raadabadi ◽  
...  
Keyword(s):  
Quality Control ◽  
Grey Relational Analysis ◽  
Sampling Method ◽  
Reward System ◽  
Factors Affecting ◽  
Relational Analysis ◽  
Fuzzy Vikor ◽  
Use Of Resources ◽  
Grey Relational ◽  
Goals And Objectives

Background: One of the techniques used to achieve productivity, employees’ job satisfaction and higher quality goods and services, as well as to solve the problems by using a team, is the formation of quality control circles. Quality control circles enable managers to meet the organization’s and employees’ needs through making effective use of resources and facilities. On the other hand, the quality of services is always affected by uncertainty and ambiguous and implicit judgments, which make its measurement uncertain. Aim: The present study aimed to identify important factors affecting the effectiveness of quality control circles in a hospital, as well as rank them using a combination of fuzzy VIKOR and Grey Relational Analysis (GRA). Methodology: This was an applied, cross-sectional and descriptive–analytical study conducted in 2016. The study population consisted of five academic members and five experts in the field of nursing working in a hospital, who were selected using a purposive sampling method. Also, a sample of 107 nurses was selected through a simple random sampling method using their employee codes and the random-number table. The required data were collected using a researcher-made questionnaire which consisted of 12 factors. The validity of this questionnaire was confirmed through giving the opinions of experts and academic members who participated in the present study, as well as performing confirmatory factor analysis. Its reliability also was verified (α=0.796). The collected data were analyzed using SPSS 22.0 and LISREL 8.8, as well as VIKOR–GRA and IPA methods. Results: The results of ranking the factors affecting the effectiveness of quality control circles showed that the highest and lowest ranks were related to ‘Managers’ and supervisors’ support’ (Š = 6.80, [Formula: see text] = 0.36) and ‘Group leadership’ (Š = 2.63, [Formula: see text] = 0.98). Also, the highest hospital performance was for factors such as ‘Clear goals and objectives’ and ‘Group cohesiveness and homogeneity’, and the lowest for ‘Reward system’ and ‘Feedback system’, respectively. Conclusion: The results showed that although ‘Training the members’, ‘Using the right tools’ and ‘Reward system’ were factors that were of great importance, the organization’s performance for these factors was poor. Therefore, these factors should be paid more attention by the studied hospital managers and should be improved as soon as possible. Applying quality control circles in any organization is very helpful and provides opportunities for maximum use of employees’ creativity, initiative and skills in reaching their and their organization’s goals and objectives, and prepares favorable working conditions for the employees’ optimal performance through increasing the managers’ sense of responsibility and commitment.

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