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Due to the importance of business process automation in industry, hyperautomation and Robotic Process Automation (RPA) attract increasing attention in the scientific field as well. Hyperautomation allows automation to do important tasks performed by business people by merging Artificial Intelligence (AI) technologies with RPA. Lomax distribution has been widely used in engineering science, service times, use and supply times of products in enterprise information systems, income and wealth data, business failure data, medical and biological science, life test and other fields. In this paper, the maximum likelihood estimation of unknown parameters of the Lomax distribution is considered using simple random sample, ranked set sample, median ranked set sample and extreme ranked set sample. Model parameters are estimated by the maximum likelihood estimation method based on Genetic Algorithm. An extensive Monte Carlo simulation study, considering perfect and imperfect ranking, is carried out in order to compare the performance of the genetic algorithm and classical optimisation method from ranked set sample-based designs with corresponding simple random sample estimators. Real-life data has been used to illustrate the application of the results obtained.

Parameter estimation of the Lomax distribution using genetic algorithm based on the ranked set samples