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

Pythagorean fuzzy multi-attribute decision making approach with incomplete weight information

“…Thus, w N j and u jk serve as the predefined weights and decision data for these methods, considering the risk preference and partial preference information. Data recollection is avoided to prevent increasing the cognitive This study then utilizes Spearman correlation coefficient to evaluate the correlation between these ordinal sequences of alternative rankings (Wang, 2024). As the value approaches 1, the two rankings indicate a strong positive correlation.…”

“…However, decision-making based on complete preference information is often idealized and expensive, which faces the challenges of time constraints, insufficient data, and expert cognitive burden. While some MADM studies incorporate fuzzy theory and grey theory to address uncertainty, they do not eliminate the data requirement Wang (2024). Moreover, methods proposed by Ahn (2015Ahn ( , 2017Ahn ( , 2024 combine extremal points of weight constraint derived from partial preference information to determine optimal decision weights but do not address MADM involving multiple experts and attributes with risk preferences.…”

“…In the past decades, multi-attribute decision-making (MADM) has been regarded as an effective tool for addressing complex decision problems, adept at tackling challenges such as objective conflicts, data diversity and high uncertainty (Resende et al, 2023;Wang, 2024). A classical problem involves selecting an optimal alternative or obtaining a global ranking from a set of alternatives under multi-attributes based on multi-expert opinions.…”

“…Unlike traditional methods, OPA avoids the need for data standardization, expert opinion aggregation, and prior weight acquisition. Additionally, various extensions of OPA have emerged, such as fuzzy OPA (Sadeghi et al, 2023;Zhao et al, 2024), rough OPA (Du et al, 2023;Kucuksari et al, 2023), Grey OPA (Mahmoudi et al, 2021), and robust OPA (Mahmoudi et al, 2022a) for handling decision data uncertainty, DGRA-OPA (Wang, 2024) and TOPSIS-OPA (Mahmoudi et al, 2022b) for addressing large-scale group decision-making, and partial OPA (Wang et al, 2024) for managing potential Pareto dominance. Consequently, OPA has garnered increasing attention recently and has been applied across various domains, including sustainable transportation evaluation (Pamucar et al, 2022), supplier evaluation (Mahmoudi et al, 2021;Mahmoudi and Javed, 2022), blockchain analysis (Sadeghi et al, 2023;Zhao et al, 2024), project portfolio selection (Mahmoudi et al, 2022a), and emergency recovery planning (Wang, 2024).…”