Pythagorean fuzzy rough decision-based approach for developing supply chain resilience framework in the face of unforeseen disruptions

Ensuring supply chain resilience (SCRES) in the face of unforeseen disruptions, such as natural disasters, geopolitical conflicts, or economic downturns, is a critical goal for decision-makers. While numerous SCRES frameworks have been proposed in existing literature, there is a lack of studies ranking these frameworks. Moreover, none of these frameworks fully satisfy all evaluation attributes. To address this research gap, three key assessment concerns need attention: the presence of multiple evaluation attributes, the varying importance levels of these attributes, and the variation in data. Therefore, multi-attribute decision analysis (MADA) methods provide effective solutions by offering sensible and logical approaches to decision-making. These methods help eliminate ambiguity and uncertainty in the information provided by a particular solution. The primary objective of this study is to propose a decision-making approach that integrates the Pythagorean Fuzzy Rough Set (PFRS) framework with the Fuzzy Weighted Zero Inconsistency Criterion (FWZIC) and the Fuzzy Decision by Opinion Score Method (FDOSM), enhancing their effectiveness in handling complex and uncertain decision-making scenarios. This study's contributions include: (1) forming an opinion decision matrix for 13 SCRES frameworks with two sets of evaluation attributes, consisting of 11 sub-attributes, 7 of which fall under SCRES Antecedents and 4 under SCRES Phases; (2) reformulating FWZIC using PFRS, referred to as the PFRS–FWZIC method, to prioritize evaluation attributes and address uncertainty in the weighting process; (3) reformulating FDOSM using PFRS, referred to as the PFRS–FDOSM method, to address multiple barrier criteria and concerns related to data variance in uncertainty evaluation; and (4) proposing a decision-based approach by integrating the PFRS–FWZIC and PFRS–FDOSM methods, based on the formed opinion decision matrix, to evaluate and rank SCRES frameworks. The effectiveness of the proposed decision-based approach is validated through sensitivity analysis and evaluated through comparison analysis, both subjectively and objectively.