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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2011, 16(2), 340-349; https://doi.org/10.3390/mca16020340

# A Novel Method for Multiple Attribute Decision-Making of Continuous Random Variable under Risk with Attribute Weight Unknown

School of Information Management Shandong Economic University, 250014 Jinan ,Shandong, China
Published: 1 August 2011
PDF [140 KB, uploaded 15 March 2016]

# Abstract

The extension of the fuzzy TOPSIS method based on the combination weight is presented to deal with multiple attribute decision-making problems under risk where the attribute value takes the form of the continuous random variable on the bounded intervals. First, the risk decision matrix is normalized by the transformation of the density function, and the variation coefficient method is used to determine the objective weights based on the expectations of the random variables. Subsequently, according to the maximizing rule of the weighted synthetic value of alternatives, the synthetic weight model is established. Then, the ideal solution and negative ideal solution is defined, the distances between the alternatives and the ideal/negative ideal solutions, and the relative closeness coefficients are calculated. In addition, the alternatives are ranked by the relative closeness coefficient of the alternatives. Finally, an illustrative example with the interval number is given to demonstrate the steps and the effectiveness of the proposed method.
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MDPI and ACS Style

Liu, P. A Novel Method for Multiple Attribute Decision-Making of Continuous Random Variable under Risk with Attribute Weight Unknown. Math. Comput. Appl. 2011, 16, 340-349.

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