Combination Weighting Method Based on Generalized Mahalanobis Distance and Weighting Relative Entropy

Abstract:

Aimed at combination weighting in multiple attribute decision making, a new approach for combining different weighting vectors is proposed. The proposed approach considers the randomicity of weights themselves and the consistency among weighting vectors, constructs a constrained weighted relative entropy model. Aimed at the disadvantage in the TOPSIS based on Euclidean distance, the TOPSIS based on Mahalanobis distance is adopted to solve the coefficients of optimal weight vector. Finally, an example is conducted and the results show the proposed approach is effective and is more reasonable than three other combination approaches.

• Periodical: Advanced Materials Research (Volumes 998-999)
• Pages: 1674-1677
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.998-999.1674
• Online since: July 2014
• Authors: Feng Zhang*, Zhen Hua Xie, Jiang Tao Cheng, Gao Lun Cui, Lin Li
• Keywords: Combination Weighting, Generalized Mahalanobis Distance, Principle of General Coherence, Relative Entropy, TOPSIS

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