P-Indeterminate Vector Similarity Measures of Orthopair Neutrosophic Number Sets and Their Decision-Making Method with Indeterminate Degrees

In the complexity and indeterminacy of decision making (DM) environments, orthopair neutrosophic number set (ONNS) presented by Ye et al. can be described by the truth and falsity indeterminacy degrees. Then, ONNS demonstrates its advantages in the indeterminate information expression, aggregations, and DM problems with some indeterminate ranges. However, the existing research lacks some similarity measures between ONNSs. They are indispensable mathematical tools and play a crucial role in DM, pattern recognition, and clustering analysis. Thus, it is necessary to propose some similarity measures between ONNSs to supplement the gap. To solve the issue, this study firstly proposes the p-indeterminate cosine measure, p-indeterminate Dice measure, p-indeterminate Jaccard measure of ONNSs (i.e., the three parameterized indeterminate vector similarity measures of ONNSs) in vector space. Then, a DM method based on the parameterized indeterminate vector similarity measures of ONNSs is developed to solve indeterminate multiple attribute DM problems by choosing different indeterminate degrees of the parameter p, such as the small indeterminate degree (p = 0) or the moderate indeterminate degree (p = 0.5) or the big indeterminate degree (p = 1). Lastly, an actual DM example on choosing a suitable logistics supplier is provided to demonstrate the flexibility and practicability of the developed DM approach in indeterminate DM problems. By comparison with existing relative DM methods, the superiority of this study is that the established DM approach indicates its flexibility and suitability depending on decision makers' indeterminate degrees (decision risks) in ONNS setting.

Automated summary

This study proposes a new method of similarity measure to supplement the gap between ONNSs, demonstrating flexibility and practicality in indeterminate multiple attribute DM problems.