Volume 6 • Issue 1 • PP: 23–28 • 2026
Generating Neutrosophic Random Variables Based on Generalized Gamma Distribution
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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In practice, we encounter many systems that cannot be studied directly, either due to high costs or because some of these systems are not directly detectable. Therefore, we resort to simulation, which involves applying the study to systems similar to real-world systems and then projecting the results if they are suitable for the real system. The simulation process requires a thorough understanding of probability distributions and the methods used to transform random numbers following a regular distribution on [0,1] into random variables that follow it. This allows us to maximize the benefits of the simulation process and obtain more accurate results for all emerging conditions. The generalized gamma distribution is a family of three parameters characterized by high flexibility. It includes several important distributions as special cases, including the gamma, Weibull, exponential, and Rayleigh distributions, making it exceptionally valuable in engineering and reliability analysis. In previous research, we presented a neutrosophic view of the process of generating random numbers and some techniques used to generate random variables. In this research, we present a neutrosophic study for generating neutrosophic random variables following the generalized gamma distribution, a distribution widely used in engineering applications. The neutrosophic approach takes into account the uncertainty and indeterminacy of the parameters, resulting in random intervals for the variables rather than specific values, and thus provides more accurate simulation results that adapt to all the conditions that the system in operation may encounter.
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References
[1] F. Smarandache and M. Jdid, “On overview of neutrosophic and plithogenic theories and applications,” Prospects for Applied Mathematics and Data Analysis, 2023.
[2] F. Smarandache, Introduction to Neutrosophic Statistics. Sitech & Education Publishing, 2014.
[3] S. Broumi and S. K. Prabha, “Fermatean neutrosophic matrices and their basic operations,” Neutrosophic Sets and Systems, vol. 58, pp. 572–595, 2023.
[4] M. M. Ismail, M. M. Ibrahim, and S. Zaki, “A neutrosophic approach for multi-factor analysis of uncertainty and sustainability of supply chain performance,” Neutrosophic Sets and Systems, vol. 58, pp. 263–277, 2023.
[5] M. Jdid, R. Alhabib, and A. A. Salama, “Fundamentals of neutrosophical simulation for generating random numbers associated with uniform probability distribution,” Neutrosophic Sets and Systems, vol. 49, pp. 92– 102, 2022.
[6] M. Jdid and N. A. Nabeeh, “Generating random variables that follow the beta distribution using the neutrosophic acceptance-rejection method,” Neutrosophic Sets and Systems, vol. 58, pp. 139–148, 2023.
[7] M. Jdid, F. Smarandache, and K. Al Shaqsi, “Generating neutrosophic random variables based gamma distribution,” Plithogenic Logic and Computation, vol. 1, pp. 16–24, 2024.
[8] E. W. Stacy, “A generalization of the gamma distribution,” The Annals of Mathematical Statistics, vol. 33, no. 3, pp. 1187–1192, 1962.
[9] M. Jdid, R. Alhabib, and A. A. Salama, “The basics of neutrosophic simulation for converting random numbers associated with a uniform probability distribution into random variables following an exponential distribution,” Neutrosophic Sets and Systems, vol. 53, pp. 358–366, 2023.
[10] I. M. Alali, Operations Research. Tishreen University Publications, 2004.
[11] J. S. Bukajh, W. Mualla et al., Operations Research. Damascus: The Arab Center for Arabization, Translation, Authoring and Publishing, 1998.
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