Maximizing deviation method for neutrosophic multiple attribute decision making with incomplete weight information. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Şahin, Rıdvan; Liu, Peide
• Is Part Of: Neural computing & applications. Volume 27:Issue 7(2016)
• Page Start: 2017
• Page End: 2029
• Publication Date: 2016-10
• Subjects: Neutrosophic sets -- Single-valued neutrosophic sets -- Interval neutrosophic sets -- Aggregation operators -- Neutrosophic multiple attribute decision making -- Maximizing deviation method; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1995-8
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract 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.