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Generalized orthopair fuzzy weighted distance‐based approximation (WDBA) algorithm in emergency decision‐making
Author(s) -
Peng Xindong,
Krishankumar Raghunathan,
Ravichandran Kattur Soundarapandian
Publication year - 2019
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22140
Subject(s) - fuzzy logic , computer science , fuzzy set , mathematical optimization , fuzzy number , set (abstract data type) , indeterminacy (philosophy) , power (physics) , algorithm , natural disaster , mathematics , artificial intelligence , physics , quantum mechanics , meteorology , programming language
With the intensification of global warming trends, the frequent occurrence of natural disasters has brought severe challenges to the sustainable development of society. Emergency decision‐making (EDM) in natural disasters is playing an increasingly important role in improving disaster response capacity. In the case of EDM evaluation, the essential problem arises serious incompleteness, impreciseness, subjectivity, and incertitude. The q‐ rung orthopair fuzzy set ( q‐ ROFS), disposing the indeterminacy portrayed by membership and nonmembership with the sum of q th power of them, is a more viable and effective means to seize indeterminacy. The aim of paper is to present a new score function of q‐ rung orthopair fuzzy number ( q‐ ROFN) for solving the failure problems when comparing two q‐ ROFNs. Firstly, we introduce some basic set operations for q‐ ROFS. The properties of these operations are also discussed in detail. Later, we propose a q‐ rung orthopair fuzzy decision‐making method based on weighted distance‐based approximation (WDBA), in which the weights of decision‐makers are obtained from a nonliner optimization model according to the deviation‐based method. Finally, some examples are investigated to illustrate the feasibility and validity of the proposed approach. The salient features of the proposed method, compared to the existing q‐ rung orthopair fuzzy decision‐making methods, are as follows: (a) it can obtain the optimal alternative without counterintuitive phenomena and (b) it has a great power in distinguishing the optimal alternative.