New Similarity and Entropy Measures of Temporal Complex Neutrosophic Set and Its application in Multi-Criteria Decision Making

Abstract

The Temporal complex neutrosophic set (TCNS) is an effective structure for dealing with information that has uncertain, indeterminate, and time-related factors in decision-making problems. In the last decade, many researchers focused on measuring the uncertainty and vagueness in knowledge using the properties of neutrosophic sets or extensions of neutrosophic sets. Information measures such as similarity and entropy measures are valuable tools for solving real-world multi-criteria decision-making (MCDM) problems. However, the existing method did not care or interest in the information measures of TCNS. This paper proposes several new similarity and entropy measures under the TCNS environment. The proposed measures have been verified and proven to comply with the axiomatic definition of the similarity and entropy measure for the TCNS. Finally, a numerical example related to choosing a tourist destination in Vietnam is given to demonstrate the practical applicability of the proposed measures. The numerical example proves that the proposed similarity and entropy measures of TCNS can produce accurate and reasonable results for decision-making problems in the real world.