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Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function

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Abstract

In this paper, we initiate a new axiomatic definition of single-valued neutrosophic distance measure and similarity measure, which is expressed by single-valued neutrosophic number that will reduce the information loss and remain more original information. Meanwhile, a novel score function is proposed. Then, the objective weights of various attributes are determined via gray system theory. Moreover, we present the combined weights, which can show both the subjective information and the objective information. Later, we present three algorithms to deal with multi-attribute decision-making problem based on revised Technique for Order Preference by Similarity to an Ideal Solution, Multi-Attributive Border Approximation area Comparison and similarity measure. Finally, the effectiveness and feasibility of approaches are demonstrated by two numerical examples.

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Acknowledgments

The authors are very appreciative to the reviewers for their precious comments which enormously ameliorated the quality of this paper. Our work is sponsored by the National Natural Science Foundation of China (No. 61163036).

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  1. School of Information Science and Engineering, Shaoguan University, Shaoguan, China

    Xindong Peng & Jingguo Dai

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  1. Xindong Peng
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  2. Jingguo Dai
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Correspondence to Xindong Peng.

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Peng, X., Dai, J. Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Comput & Applic 29, 939–954 (2018). https://doi.org/10.1007/s00521-016-2607-y

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