Multi-attribute Cognitive Decision Making via Convex Combination of Weighted Vector Similarity Measures for Single-Valued Neutrosophic Sets

The fuzzy Weighted Influence Nonlinear Gauge System method extended with D numbers and MICMAC

The Weighted Influence Nonlinear Measurement System (WINGS) method originates from DEMATEL, which has the advantage of analyzing the interweaved determinants and the causal relationships within them. The innovation is mainly reflected in considering both the strength of the influencing factors themselves and the relationship of their mutual influence. To address the problems of ambiguity in assessing information and uncertainty in the judgment of expert group, this paper proposes fuzzy WINGS improved by D numbers (fuzzy D-WINGS). Combining D numbers with Triangular fuzzy numbers can overcome the limitation of mutually exclusive and collectively extensive set. The WINGS method is used to reveal the interdependent causal relationships by recognizing the orientation and strength of the factors. Utilizing the MICMAC method to draw matrix analysis diagrams can further reveal the relationship among them. Finally, a practical case study is conducted to prove the practicability of this fuzzy D-WINGS–MICMAC method.

Development of a Relative Similarity Degree Based Engineering Construction Multi-Attribute Decision Model and Its Application

Generally, there are large amounts of uncertain factors in the multi-attribute decision system. By using the gray relational degree and fuzzy gray relational degree, the weights of the comprehensive indexes are extracted. Then, a novel decision model is established based on the concept of relative similarity degree. Finally, comparative research is carried out taking the maritime safety engineering construction in Hunan Province, China, as an example to verify that the developed model is rather effective and practical for its high resolution and sensitivity in multi-attribute decision.