Journal
DEFENCE TECHNOLOGY
Volume 16, Issue 1, Pages 208-216Publisher
ELSEVIER
DOI: 10.1016/j.dt.2019.06.019
Keywords
MAGDM problems; Q-rung orthopair fuzzy sets (q-ROFSs); MABAC model; Q-rung orthopair fuzzy weighted average (q-ROFWA) operators; Q-rung orthopair fuzzy weighted geometric (q-ROFWG) operators; Q-ROFNs MABAC model; Construction project
Categories
Funding
- National Natural Science Foundation of China [71571128]
- Humanities and Social Sciences Foundation of Ministry of Education of the People's Republic of China [14XJCZH002, 15YJCZH138]
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As the generalization of intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS), the q-rung orthopair fuzzy set (q-ROFS) has emerged as a more meaningful and effective tool to solve multiple attribute group decision making (MAGDM) problems in management and scientific domains. The MABAC (multi-attributive border approximation area comparison) model, which handles the complex and uncertain decision making issues by computing the distance between each alternative and the bored approximation area (BAA), has been investigated by an increasing number of researchers more recent years. In our article, consider the conventional MABAC model and some fundamental theories of q-rung orthopair fuzzy set (q-ROFS), we shall introduce the q-rung orthopair fuzzy MABAC model to solve MADM problems. at first, we briefly review some basic theories related to q-ROFS and conventional MABAC model. Furthermore, the q-rung orthopair fuzzy MABAC model is built and the decision making steps are described. In the end, An actual MADM application has been given to testify this new model and some comparisons between this novel MABAC model and two q-ROFNs aggregation operators are provided to further demonstrate the merits of the q-rung orthopair fuzzy MABAC model. (C) 2020 China Ordnance Society. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co.
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