A framework for resilience assessment in process systems using a fuzzy hybrid MCDM model. (March 2021) - Zarei, Esmaeil; Ramavandi, Bahman; Darabi, Amir Hossein; Omidvar, Mohsen
Abstract: Resilience engineering (RE) has recently emerged as a novel safety management paradigm in socio-technical organizations. It is believed that RE is more compatible with the characteristics of complex socio-technical systems. The multicriteria nature and the presence of both qualitative and quantitative latent factors make RE substantially more complex especially in quantifying and modeling aspects. To address this issue, the present research aims to develop a fuzzy hybrid multicriteria decision-making (MCDM) model for quantifying and evaluating resilience using the fuzzy Analytic hierarchy process (F-AHP) and fuzzy VIKOR (F-VIKOR) techniques. Initially, an evaluation framework including six resilience indicators and 43 sub-indicators was established. Afterward, the F-AHP method was used to determine the weight of the resilience indicators, while the F-VIKOR method was employed to rank the resilience performance of the different operational units. To present the model capability, we evaluated the resilience of a gas refinery as a typical instance of socio-technical systems. The findings revealed the performance level of resilience indicators in all units of the studied refinery and their ranking based on the computation of the index value (Qi). With respect to the Qi values, the best and worst performance of units from the resilience perspective was specified. Results indicate that the proposed model can serve as an effective evaluation approach in complicated systems and can be used to effectively design strategies to improve system safety performance. To the best of our knowledge, this is the first study that evaluates the resilience using the VIKOR and AHP in a fuzzy environment in the process industry. Highlights: A new method for resilience engineering assessment based on AHP-VIKOR was presented. Six resilience engineering (RE) indicators and 43 sub-indicators were considered. To handle the inherent uncertainty of subjective judgments, fuzzy set theory was applied. Poor units of the plant were identified and ranked with respect to the RE indicators.