Resilience-Based Efficiency Measurement of Process Industries with Type-2 Fuzzy Sets

Abstract

Today’s business environments are prone to high levels of uncertainties and risks because the speed of changes is too high. Hence, the way of dealing with threats has changed. Complex socio-technical systems (CSSs) are confronted with out-of-control disturbances that need risk management systems to be applied. Risk management is organized activities to monitor and control risks in CSSs. Newly, resilience engineering (RE) as a safety management discipline has been expanded to change the attitude to risks. RE is defined as the capability of a system to absorb disturbances and adapt to rapidly changing conditions. Process industries as CSS, are potentially disposed to catastrophic events such as explosions, toxic leakages, stoppages of production operations, etc. In this paper, a novel RE-based algorithm is developed to measure the efficiency of process industries for preventing and reducing disorders considering their high-level of uncertainty. To do this, firstly, resilience indices in an oil refinery as an essential process industry are extracted. Then, using type-2 fuzzy data envelopment analysis (DEA), a new multi-objective approach is presented to evaluate the efficiency of DMUs from the perspective of RE. Interval type-2 fuzzy sets (IT2 FSs) is used in this approach because they potentially hold more implication than the classical type-1 fuzzy sets (T1 FSs). As a case study, a refinery as a CSS is evaluated. Using the proposed algorithm shows the efficiency scores of DMUs in the refinery. By conducting a comparative analysis, it was revealed that the proposed model outperforms classical methods. The results of the proposed approach show that besides simplicity, there is good stability to consider the criteria of RE in efficiency measurement with IT2 FSs.