An improved MCDM model with cloud TOPSIS method
Jianing Zhang, Wenbing Chang, Shenghan Zhou · 2015
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.