Maximizing deviation method for neutrosophic multiple attribute decision making with incomplete weight information

• Published in: Neural computing & applications Vol. 27; no. 7; pp. 2017 - 2029
• Main Authors: Şahin, Rıdvan, Liu, Peide
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2016; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Decision making; Deviation; Hierarchies; Image Processing and Computer Vision; Maximization; Multiple criteria decision making; Operators (mathematics); Optimization; Original Article; Probability and Statistics in Computer Science; Single-valued neutrosophic sets; Aggregation operators; Interval neutrosophic sets; Neutrosophic sets; Maximizing deviation method; Neutrosophic multiple attribute decision making
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-015-1995-8

This paper develops a method for solving the multiple attribute decision-making problems with the single-valued neutrosophic information or interval neutrosophic information. We first propose two discrimination functions referred to as score function and accuracy function for ranking the neutrosophic numbers. An optimization model to determine the attribute weights that are partly known is established based on the maximizing deviation method. For the special situations where the information about attribute weights is completely unknown, we propose another optimization model. A practical and useful formula which can be used to determine the attribute weights is obtained by solving a proposed nonlinear optimization problem. To aggregate the neutrosophic information corresponding to each alternative, we utilize the neutrosophic weighted averaging operators which are the single-valued neutrosophic weighted averaging operator and the interval neutrosophic weighted averaging operator. Thus, we can determine the order of alternatives and choose the most desirable one(s) based on the score function and accuracy function. Finally, some illustrative examples are presented to verify the proposed approach and to present its effectiveness and practicality.