Exploring Generalized Goel-Okumoto Process Parameters Estimation Strategies with Application
DOI:
https://doi.org/10.62933/k6wr1g09Keywords:
Generalized Goel-Okumoto process, , Non-homogeneous Poisson process,, Maximum likelihood estimator,, Shrinkage method, , ABC algorithmAbstract
Estimating the parameters of stochastic processes is essential to understand the behavior of processes and make correct decisions. In this paper, the Generalized Goel-Okumoto process (GGOP) was used, which provides flexible frameworks to capture the behavior of time-dependent systems, especially in the non-homogeneous Poisson process (NHPP), and its parameters were estimated using important traditional methods, namely the Maximum Likelihood (MLE) method and the Shrinkage (SH) method. An intelligent method represented by the Artificial Bee Colony algorithm (ABC) was proposed. This paper will contribute to a comparison between traditional and intelligent methods to estimate the parameters of the process under study, and to enhance the research results in understanding stochastic processes and supporting better decision-making, a realistic application was conducted on the shutdowns of the Badush Expansion Plant in Nineveh Governorate for the period from 1\1\2024 - 25\8\2024. It was found that the ABC algorithm in estimating the time rate of the GGOP was better than the maximum likelihood estimator and the shrinkage method.
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