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Novel neutrosophic Dombi Bonferroni mean operators with mobile cloud computing industry evaluation
Author(s) -
Peng Xindong,
Smarandache Florentin
Publication year - 2019
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12411
Subject(s) - indeterminacy (philosophy) , operator (biology) , computer science , cloud computing , ranking (information retrieval) , topsis , ambiguity , artificial intelligence , data mining , operations research , mathematics , biochemistry , chemistry , physics , repressor , quantum mechanics , transcription factor , gene , programming language , operating system
In the age of mobile cloud computing, we are confronted by mobility, diversity of network access types, frequent network disconnection and poor reliability, and security with complex structures. Mobile cloud computing industry decision making is crucially important for countries or societies to enhance the effectiveness and validity of leadership, which can greatly expedite industrialized and large‐scale development. In the case of mobile cloud computing industry decision evaluation, the indispensable issue arises serious inexactness, fuzziness, and ambiguity. Single‐valued neutrosophic set, disposing the indeterminacy portrayed by truth membership T , indeterminacy membership I , and falsity membership F , is a more viable and effective means to seize indeterminacy. The main purpose of the current paper is to investigate the novel operations on single‐valued neutrosophic number (SVNN) based on Dombi Bonferroni mean (DBM) and Dombi geometric Bonferroni mean (DGBM) operator, which have the enormous advantage of high flexibility with adjustable parameters. Moreover, we employ the DBM operator to present single‐valued neutrosophic DBM (SVNDBM) operator, single‐valued neutrosophic weighted DBM (SVNWDBM) operator, single‐valued neutrosophic DGBM (SVNDGBM), operator and single‐valued neutrosophic weighted DGBM (SVNWDGBM) operator for disposing with the aggregation of SVNNs and develop two multiple attribute decision making methods based on SVNWDBM and SVNWDGBM. The validity of algorithms are illustrated by a mobile cloud computing industry decision making issue, along with the sensitivity analysis of diverse parameters on the ranking. Finally, a comparison of the developed with the existing single‐valued neutrosophic decision making methods has been executed for displaying their effectiveness.