Multi-attribute decision-making method based on Taylor expansion
Determining attribute weights is an indispensable step in multi-attribute decision-making problems, and it is also a top priority in the study of multi-attribute decision-making problems. Existing methods for determining attribute weights do not completely and effectively reflect the decision-maker’...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719836078 |
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| Summary: | Determining attribute weights is an indispensable step in multi-attribute decision-making problems, and it is also a top priority in the study of multi-attribute decision-making problems. Existing methods for determining attribute weights do not completely and effectively reflect the decision-maker’s dependency preferences, which will result in unreasonable ranking results for decision-makers. To solve this problem, this article proposes a feature-weighted multi-attribute decision-making method based on Taylor expansion. The method uses the natural base and the eigenvalues of the matrix to construct the feature-weighted coefficients and weights; normalizes all the feature vectors of the matrix; and constructs a new weight vector. Combined with the example to analyze and verify, the method makes reasonable use of all decision information, which saves the decision time of decision-makers. |
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| ISSN: | 1550-1477 |