Affiliations: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Correspondence:
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Corresponding author: Shuaishuai Lin, School of Economics and Management, North China Electric Power University, Beijing 102206, China. E-mail: 18811306733@163.com.
Abstract: The selection of electrical equipment condition-based maintenance alternatives is a multi-attribute decision-making problem. Choosing the proper maintenance scheme can not only accurately grasp the operation state of power equipment, but also weaken the blindness of maintenance work and improve economic benefits. Therefore, it is particularly important to choose a scientific decision-making method. In this paper, a multi-attribute decision-making method based on cloud model and grey D-S evidence theory is proposed. Firstly, cloud model is applied to deal with qualitative criteria, which reduces the fuzziness and randomness of qualitative language and remains linguistic information as much as possible in the transformation process. Secondly, on the basis of the concept of grey correlation degree, a new method to calculate basic probability assignment (BPA) or mass function in D-S evidence theory is presented which diminishes the grey character in decision-making process. Finally, the example analysis and sensitivity analysis verify the effectiveness and practicability of the proposed model.
Keywords: Cloud model, grey correlation, D-S evidence theory, basic probability assignment, information entropy
