Non-linear programming method for multi-criteria decision making problems under interval neutrosophic set environment

Interval neutrosophic sets (INSs) capturing the uncertainties by characterizing into the intervals of the truth, the indeterminacy, and the falsity membership degrees, is a more flexible way to explore the decision-making applications. In this paper, we develop a nonlinear programming (NP) model bas...

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Vydáno v:	Applied intelligence (Dordrecht, Netherlands) Ročník 48; číslo 8; s. 2199 - 2213
Hlavní autoři:	Garg, Harish, Nancy
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.08.2018 Springer Nature B.V
Témata:	Artificial Intelligence Computer Science Decision making Linear programming Machines Manufacturing Mathematical programming Mechanical Engineering Multiple criteria decision making Nonlinear programming Processes Interval neutrosophic set Neutrosophic set TOPSIS Multi criteria decision-making Nonlinear programming model
ISSN:	0924-669X, 1573-7497
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Abstract	Interval neutrosophic sets (INSs) capturing the uncertainties by characterizing into the intervals of the truth, the indeterminacy, and the falsity membership degrees, is a more flexible way to explore the decision-making applications. In this paper, we develop a nonlinear programming (NP) model based on the technique for order preference by similarity to ideal solution (TOPSIS), to solve decision-making problems in which criterion values and their importance are given in the form of interval neutrosophic numbers (INNs). Based on the concept of closeness coefficient, we firstly construct a pair of the nonlinear fractional programming model and then transform it into the linear programming model. Furthermore, to determine the ranking of considered alternatives, likelihood-based comparison relations are constructed. Finally, an illustrative example demonstrates the applicability of the proposed method for dealing with decision making problems with incomplete knowledge.
AbstractList	Interval neutrosophic sets (INSs) capturing the uncertainties by characterizing into the intervals of the truth, the indeterminacy, and the falsity membership degrees, is a more flexible way to explore the decision-making applications. In this paper, we develop a nonlinear programming (NP) model based on the technique for order preference by similarity to ideal solution (TOPSIS), to solve decision-making problems in which criterion values and their importance are given in the form of interval neutrosophic numbers (INNs). Based on the concept of closeness coefficient, we firstly construct a pair of the nonlinear fractional programming model and then transform it into the linear programming model. Furthermore, to determine the ranking of considered alternatives, likelihood-based comparison relations are constructed. Finally, an illustrative example demonstrates the applicability of the proposed method for dealing with decision making problems with incomplete knowledge.
Author	Garg, Harish Nancy
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Cites_doi	10.1109/TFUZZ.2006.890678 10.1016/j.cie.2016.08.017 10.1186/s40064-016-2591-9 10.1615/Int.J.UncertaintyQuantification.2016018441 10.1016/j.apm.2010.11.025 10.1080/03081070600574353 10.1007/s13042-015-0432-8 10.1002/int.21809 10.1016/j.artmed.2014.12.007 10.1016/S0165-0114(86)80034-3 10.1007/s00521-015-2131-5 10.1016/j.asoc.2015.10.040 10.5958/2320-3226.2014.00006.X 10.1080/00207721.2014.994050 10.1002/int.21860 10.1016/0165-0114(89)90205-4 10.1016/S0165-0114(98)00333-9 10.1007/s00521-014-1688-8 10.1615/Int.J.UncertaintyQuantification.2016018603 10.1016/j.asoc.2011.01.011 10.1016/S0019-9958(65)90241-X 10.1109/TFUZZ.2010.2065812 10.1002/int.20386 10.3233/IFS-120724 10.3233/IFS-130810 10.1109/CCDC.2016.7531710
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SubjectTerms	Artificial Intelligence Computer Science Decision making Linear programming Machines Manufacturing Mathematical programming Mechanical Engineering Multiple criteria decision making Nonlinear programming Processes
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Title	Non-linear programming method for multi-criteria decision making problems under interval neutrosophic set environment
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