Uncertainty-driven generation of neutrosophic random variates from the Weibull distribution.

Objective: This paper aims to introduce an algorithm designed for generating random variates in situations characterized by uncertainty. Method: The paper outlines the development of two distinct algorithms for producing both minimum and maximum neutrosophic data based on the Weibull distribution. Results: Through comprehensive simulations, the efficacy of these algorithms has been thoroughly assessed. The paper includes tables presenting neutrosophic random data and an in-depth analysis of how uncertainty impacts these values. Conclusion: The study's findings demonstrate a noteworthy correlation between the degree of uncertainty and the neutrosophic minimum and maximum data. As uncertainty intensifies, these values exhibit a tendency to decrease. [ABSTRACT FROM AUTHOR]