4.7 Article

The context-based distance measure for intuitionistic fuzzy set with application in marine energy transportation route decision making

期刊

APPLIED SOFT COMPUTING
卷 101, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.asoc.2020.107044

关键词

Context-based distance; Distance measure; Similarity measure; Decision making method; Intuitionistic fuzzy set

资金

  1. National Natural Science Foundation of China [72071135]

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This paper proposes a context-based distance measure for intuitionistic fuzzy sets and defines a new similarity measure to enhance discrimination capability. The effectiveness of these methods is validated through a practical case study, demonstrating their fine discrimination ability and effectiveness.
Background and motivation: The distance measure is a classical topic in the intuitionistic fuzzy set theory. Although plenty of distance measures have been proposed and successfully applied to the decision-making problems, it is found that there still exists the counter-intuitive phenomenon where the context information in the alternatives is seldom considered in the existing distance measures. Methods: A context-based distance measure for the intuitionistic fuzzy set is proposed to solve this problem in this paper. The domination and competition relationships of the alternatives are integrated into the new distance measure. To fully take advantage of the proposed distance measure, a new similarity measure that utilizes the nonlinear function of the distance and additional parameters to control its discrimination capability is also defined. To demonstrate the effectiveness of the proposed information measures and their practical applications, an extended decision making method based on the order preference by similarity to ideal solutions is proposed. Results: A practical case study of the marine energy transportation route decision making problem is provided as the validation. The comparative results comparing with other methods demonstrate the fine discrimination ability and effectiveness of the proposed methods. (C) 2020 Elsevier B.V. All rights reserved.

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