Resilience-sustainability assessment of electricity supply chain under uncertainty: a new fuzzy dynamic network DEA based on rang adjusted measure

The purpose of this study is to propose a new dynamic network-based data envelopment analysis approach to appraise the efficiency of Iran’s electricity supply chains in terms of sustainability and resilience paradigms over consecutive periods. The proposed dynamic network-based data envelopment analysis model is developed in a Rang Adjusted Measure framework as it can well handle both the desirable and undesirable outputs and has several properties such as translation invariant, unit invariant, and projection. Then to deal with qualitative and imprecise data, the proposed model is extended in a fuzzy context. The network-based and dynamic features of the suggested model make it possible to appraise the efficiency of the whole electricity supply chains and its components simultaneously at multi-period planning. The dynamic feature allows the suggested model to recognize changes in the efficiency of the Iran’s electricity supply chains much better than static data envelopment analysis models. The suggested model comprehensively measures the efficiency of Iran’s electricity supply chains by taking into account sustainability and resilience paradigms using real data. Given the research findings, the privatization of companies has had a significant impact on the performance of Iran’s electricity supply chains in 2020. Also, the results of this study show that the average efficiency of the transmission sector from 2017 to 2020 is higher than the two other generation and distribution sectors. Finally, this study suggests a way to improve the sustainability and resilience of Iran’s electricity supply chains. The proposed methodology can be applied to electricity supply chains of other countries as well.This study introduces a new fuzzy dynamic network data envelopment analysis model and apply it to appraise the sustainability and resilience of the Iran’s electricity supply chains during 2017–2020 years.