摘要：Optimizing the maintenance activities coordination-scheduling can be a daily challenge in any technical system and becomes more difficult when the size of the system is large and economic and logistic variables are involved. In general, as we can guess, the coordination-scheduling process modelling can be defined as an optimization problem once the target or goal to be met and the system model is well known. In order to address the above statements, well-defined methodologies have been proposed to model any coordination-scheduling process. Among them, simulation-based risk assessment is a comprehensive, flexible, and adaptable methodology to solve this problem. In this paper, we generalize a risk assessment approach and analyze case studies in different domains where the methodological model remains the same, but the parameters, variables and system under study are constantly changing. The article builds, based on different case studies, a general, adaptive, and simple simulation-based risk assessment model to optimize the coordination scheduling of maintenance tasks, which is able to intuitively and easily calibrate data-driven variables and parameters using a machine learning approach. Maintenance activities optimization via modelling approach is dedicated to manufacturing-distribution systems and is presented based on selected case studies.
