A resilience assessment framework for complex engineered systems using graphical evaluation and review technique (GERT)
Sunyue Geng (TU Delft - Technology, Policy and Management, Nanjing University of Aeronautics and Astronautics)
Ming Yang (TU Delft - Technology, Policy and Management, Universiti Teknologi Malaysia)
Mihaela Mitici (Universiteit Utrecht)
Sifeng Liu (Nanjing University of Aeronautics and Astronautics)
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Abstract
System resilience characterizes the capability of maintaining the required functionality under disruptions, which is of great significance in evaluating the productivity and safety of complex engineered systems. Although most studies conduct resilience assessment from qualitative and quantitative perspectives, system functionality that reflects functional requirements for complex engineered systems needs to be elaborated. In addition, given that complex engineered systems achieve dynamic performance during disruptions, measuring the actual performance under uncertainty is imperative. To this end, this paper develops a quantitative framework to assess the resilience of complex engineered systems. The developed framework comprises three phases, functionality analysis, performance evaluation, and resilience assessment. Firstly, system functionality is analyzed using a functional tree illustrating the relationship between functions. The overall objective, primary functions, and sub-functions are identified according to task requirements. Secondly, system performance is quantified considering uncertain factors through Graphical Evaluation and Review Technique (GERT). Probabilistic branches and network logic are employed to represent the implementation of various functions. Finally, resilience assessment is carried out from the perspectives of anticipation, absorption, adaptation, and restoration abilities. A case study on the satellite network shows the effectiveness of the developed framework. The developed framework determines system functionality based on task requirements, evaluates system performance with limited information, and accurately assesses system resilience.
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