An improved MCDM model with cloud TOPSIS method

The paper aims to develop an improved MCDM model with cloud TOPSIS method. In the past literature, many methods were used to deal with the complexity and uncertainty of the world in Multiple-criteria decision-making process, such as the linguistic variable, fuzzy set and so on. However, linguistic c...

Full description

Saved in:
Bibliographic Details
Published inThe 27th Chinese Control and Decision Conference (2015 CCDC) pp. 873 - 878
Main Authors Jianing Zhang, Wenbing Chang, Shenghan Zhou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2015
Subjects
Cloud Model
Cognition
Decision making
Entropy
Expert systems
Human Cognition
Linguistic Concept
MCDM
Pragmatics
TOPSIS
Transforms
Uncertainty
Online AccessGet full text

Cover

More Information
Summary:The paper aims to develop an improved MCDM model with cloud TOPSIS method. In the past literature, many methods were used to deal with the complexity and uncertainty of the world in Multiple-criteria decision-making process, such as the linguistic variable, fuzzy set and so on. However, linguistic concept, as an effective tool to describe human cognition, has both fuzziness and randomness, which cannot be dealt with very well by traditional methods. Cloud Model, the very method to handle both fuzziness and randomness of the linguistic concept, is specially imported into the TOPSIS to solve the fuzziness and randomness in decision-making. In order to achieve the Cloud TOPSIS, the difference of Cloud is proposed; meanwhile PIC (Positive Ideal Cloud) and NIC (Negative Ideal Cloud) are defined. Finally, the method of Cloud TOPSIS is demonstrated applicable and effective, compared with the TOPSIS method based on interval data. The result suggests that the improved model has better distinction degree.
ISSN:1948-9439
DOI:10.1109/CCDC.2015.7162042