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RELATIVE RATIO METHOD FOR MULTIPLE ATTRIBUTE DECISION MAKING PROBLEMS

International Journal of Information Technology & Decision Making, 2009
The multiple attribute decision making (MADM) is an important research field in decision science and operations research. Recently, several commonly used methods such as the TOPSIS and the VIKOR were proposed to solve the MADM problems. The TOPSIS and VIKOR are based on aggregating functions representing closeness to the ideal, which originated in the
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Fuzzy Multiple Attribute Decision Making Methods

1992
A MADM problem is given as: $$\text{D=}\begin{matrix} {{\text{A}}_{1}}\\ {{\text{A}}_{2}}\\ \vdots\\ {{\text{A}}_{\text{m}}}\\ \end{matrix}\text{ }\left[\begin{matrix} {{\text{X}}_{1}}\text{ }{{\text{X}}_{2}}\text{ }\cdots \text{ }{{\text{X}}_{\text{n}}}\\ {{\text{X}}_{11}}\text{ }{{\text{X}}_{12}}\text{ }\cdots \text{ }{{\text{X}}_{1\text{n}}}\\ {{
Shu-Jen Chen, Ching-Lai Hwang
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Applying fuzzy multiple attributes decision making for product configuration

Journal of Intelligent Manufacturing, 2008
Mass customization (MC) is an emergent concept in industry intended to provide customized products through flexible processes in high volumes and at reasonably low costs. The method of configuration is one of important ways to realize quickly product customization. But, in business, particularly through the Internet, a customer normally develops in his
Bin Zhu   +4 more
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A statistical approach to multiple-attribute decision-making with interval numbers

International Journal of Systems Science, 2003
In multiple-attribute decision-making, the overall values of alternatives would be interval numbers due to the inherent uncertain property of the problems in the ambiguous decision domain. Bryson and Mobolurin proposed the use of linear programming models to compute attribute weights and overall values of the alternatives in the form of interval ...
Quan Zhang   +3 more
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TOPSIS method for hesitant fuzzy multiple attribute decision making

Journal of Intelligent & Fuzzy Systems, 2014
The cooperative learning appeared in the globalization of education, it is a new teaching theory and strategy frequently used to study the new things and to solve the new problems by group cooperation. The introduction of cooperative learning to fundamental curriculum reform has been a main direction of various countries' education reforms and develops.
Xie Feng   +3 more
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Multiple attributes group decision making under fuzzy environment

SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), 2004
Under the circumstance that opinions of members or group decision making are assumed as positive fuzzy numbers, the authors propose a new approach to aggregate the experts' opinions based on a measure near degree and an absolute value of expectation difference of fuzzy numbers.
Ji-bin Lan, Yang Xu 0001, Jia-Zhong Liu
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Multiple attribute decision making problems with mixed uncertain information

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010
This paper presents a novel method for multiple attribute decision making problems, in which interval numbers are used to assess the weights of attributes, and fuzzy linguistic variables are utilized to evaluate the suitability of alternatives versus various attributes.
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Multiple Attribute Decision Making in the Manufacturing Environment

2012
Manufacturing can be defined as the application of mechanical, physical, and chemical processes to modify the geometry, properties, and/or appearance of a given starting material in the making of new, finished parts or products. This effort includes all intermediate processes required for the production and integration of a product’s components ...
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Improved Multiple Attribute Decision Making Methods

2012
The improved multiple attribute decision making methods for decision making in the manufacturing environment are described in this chapter.
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Multiple Attribute Decision Making

2011
Gwo-Hshiung Tzeng, Jih-Jeng Huang
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