scholarly journals A comparison of TOPSIS, grey relational analysis and COPRAS methods for machine selection problem in the food industry of Turkey

2021 ◽  
Vol 9 (2) ◽  
pp. 81
Author(s):  
Sami Özcan ◽  
Ali Kemal Çelik
Keyword(s):  
Decision Making ◽  
Grey Relational Analysis ◽  
Entropy Method ◽  
Sensitivity Analyses ◽  
Food Sector ◽  
Relational Analysis ◽  
Machine Selection ◽  
Order Preference ◽  
Topsis Technique ◽  
Grey Relational

<div data-canvas-width="540.0693333333334">The paper aims to compare the results of the selection/choice of cream separators by using multi-criteria decision-making methods in an integrated manner for an enterprise with a dairy processing capacity of 80 to 100 tons per day operating in the Turkish food sector. A total of 7 alternative products and 7 criteria for milk processing were determined. Criterion weights were calculated using entropy method and then integrated into TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions), GRA (Grey Relational Analysis) and COPRAS (Complex Proportional Assessment) methods. Sensitivity analyses were carried out on the results obtained from the three methods to check for their reliability. At the end of the study, similar alternative and appropriate results were found from the TOPSIS and COPRAS methods. However, different alternative but appropriate or suitable results were obtained from the GRA method. Sensitivity analysis of the three methods showed that all the methods used were valid. In the review of available and related literature, very few studies on machine selection in the dairy and food sector in general were found. For this reason, it is thought that the study will contribute to the decision-making process of companies in the dairy sector in their choice of machinery selections. As far as is known, this paper is the first attempt in extant literature to compare in an integrated manner the results of TOPSIS, COPRAS and GRA methods considered in the study.</div>

scholarly journals Personalized Product Evaluation Based on GRA-TOPSIS and Kansei Engineering

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 867 ◽  
Author(s):  
Huafeng Quan ◽  
Shaobo Li ◽  
Hongjing Wei ◽  
Jianjun Hu
Keyword(s):  
Game Theory ◽  
Grey Relational Analysis ◽  
Evaluation System ◽  
Product Evaluation ◽  
Entropy Method ◽  
Kansei Engineering ◽  
Topsis Method ◽  
Decision Matrix ◽  
Relational Analysis ◽  
Grey Relational

With the improvement of human living standards, users’ requirements have changed from function to emotion. Helping users pick out the most suitable product based on their subjective requirements is of great importance for enterprises. This paper proposes a Kansei engineering-based grey relational analysis and techniques for order preference by similarity to ideal solution (KE-GAR-TOPSIS) method to make a subjective user personalized ranking of alternative products. The KE-GRA-TOPSIS method integrates five methods, including Kansei Engineering (KE), analytic hierarchy process (AHP), entropy, game theory, and grey relational analysis-TOPSIS (GRA-TOPSIS). First, an evaluation system is established by KE and AHP. Second, we define a matrix variate—Kansei decision matrix (KDM)—to describe the satisfaction of user requirements. Third, the AHP is used to obtain subjective weight. Next, the entropy method is employed to obtain objective weights by taking the KDM as input. Then the two types of weights are optimized using game theory to obtain the comprehensive weights. Finally, the GRA-TOPSIS method takes the comprehensive weights and the KMD as inputs to rank alternatives. A comparison of the KE-GRA-TOPSIS, KE-TOPSIS, KE-GRA, GRA-TOPSIS, and TOPSIS is conducted to illustrate the unique merits of the KE-GRA-TOPSIS method in Kansei evaluation. Finally, taking the electric drill as an example, we describe the process of the proposed method in detail, which achieves a symmetry between the objectivity of products and subjectivity of users.

scholarly journals Using Grey Relational Analysis with Entropy to Predict the International Financial Center of China

2017 ◽  
Vol 5 (1) ◽  
pp. 88-96 ◽  
Author(s):  
Hang Jiang ◽  
Jan-Yan Lin ◽  
Peng Jiang
Keyword(s):  
Economic Growth ◽  
Financial Development ◽  
Grey Relational Analysis ◽  
Direct Investment ◽  
Entropy Method ◽  
Relational Analysis ◽  
Key Indicators ◽  
Grey Relational ◽  
International Financial Center ◽  
Total Stock

Abstract The establishment of the China Pilot Free Trade Zone (FTZ) has significantly promoted international trade, financial development, and economic growth. Building international financial centers (IFCs) satisfies the demand for FTZs to facilitate financial development, as well as promoting economic growth. Thus, successfully predicting the next IFC in China under the FTZ framework is an important issue. In this study, we applied grey relational analysis combined with entropy method to predict potential IFCs among seven FTZ cities. According to the results, our interesting findings include: 1) the “total stock turnover”, “total value of imports and exports”, and “Foreign Direct Investment (FDI)” are key indicators for determining future IFCs; 2) among seven cities, Shenzhen and Tianjin are highly likely to become the next IFCs, while Shanghai is already an IFC.

A New Method of Multi-Attribute Decision-Making Based on Grey Relational Analysis with Time Series

2013 ◽  
Vol 444-445 ◽  
pp. 666-670
Author(s):  
Yi Zhang
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Grey Relational Analysis ◽  
Uncertain Information ◽  
Decision Method ◽  
Rational Decision Making ◽  
Relational Analysis ◽  
Multi Attribute Decision Making ◽  
Subjective Views ◽  
Grey Relational

For the multi-attribute decision making with time series, based on the comprehensive consideration of various indicators of quality and growth degree, with grey relational analysis on less data, uncertain information can be integrated comparison, so we put forward a new decision method considering the decision maker subjective views, and provide a scientific and rational decision-making method. By the example, that method is viable.

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