A hybrid MADM method considering expert consensus for emergency recovery plan selection: Dynamic grey relation analysis and partial ordinal priority approach

Emergency recovery plan selection (ERPS) is critical for managing post-disaster recovery and ensuring long-term societal stability. However, current multi-attribute decision-making (MADM) research on ERPS is limited and lacks consideration of Pareto-optimal solutions and expert consensus resulting from multi-stakeholder involvement. Therefore, this study proposes a hybrid Dynamic Grey Relation Analysis and Partial Ordinal Priority Approach (DGRA-POPA) model for ERPS. The proposed approach employs stable and easily accessible ranking data as inputs. DGRA is first utilized to extract consistency in attribute preferences among experts and serves as the basis for determining expert rankings. Considering expert consensus and information distribution, preference modification coefficients are derived and embedded into POPA. Through decisionweight optimization, partial-order cumulative transformation, and dominance structure generation, the weights for experts, attributes, and alternatives are determined along with a Hasse diagram. This diagram offers Pareto-optimal and suboptimal alternatives and alternative clustering information. The proposed approach is demonstrated using the ERPS after the Manchester Stadium attack. Sensitivity and comparative analyses with ten different MADM methods validate the effectiveness. Overall, the proposed approach enhances ERPS transparency, stability, and robustness by identifying Pareto-optimal alternatives while considering expert consensus and information distribution.