[EN] Based on the previous literature about the random logistic and Gompertz models, the aim of this paper is to extend the investigations to the generalized logistic differential equation in the random setting. First, this is done by rigorously constructing its solution in two different ways, namely, the sample-path approach and the mean-square calculus. Secondly, the probability density function at each time instant is derived in two ways: by applying the random variable transformation technique and by solving the associated Liouville's partial differential equation. It is also proved that both the stochastic solution and its density function converge, under specific conditions, to the corresponding solution and density function of the logistic and Gompertz models, respectively. The investigation finishes showing some examples, where a number of computational techniques are combined to construct reliable approximations of the probability density of the stochastic solution. In particular, we show, step-by-step, how our findings can be applied to a real-world problem. (c) 2022 The Author(s). Published by Elsevier B.V.This work has been supported by the Spanish Agencia Estatal de Investigacion grant PID2020-115270GB-I00. Vicente Bevia acknowledges the doctorate scholarship granted by Programa de Ayudas de Investigacion y Desarrollo (PAID), Universitat Politecnica de Valencia.Bevia, V.; Calatayud-Gregori, J.; Cortés, J.; Jornet-Sanz, M. (2023). On the generalized logistic random differential equation: Theoretical analysis and numerical simulations with real-world data. Communications in Nonlinear Science and Numerical Simulation. 116. https://doi.org/10.1016/j.cnsns.2022.10683211
