Multidimensional decision-making has substituted traditional decision-making due to the increased risk and complexity involved in the decision processes and cognitive behaviors. Moreover, uncertainty management is necessary in the decision-making processes that involve the degree of confidence of the experts. Conflict assessment and resolution are paramount to the smooth functioning of the industrial ecosystem in such an automated dynamic environment. This research aims to create a multi-attribute decision-making (MADM) model in a hybrid fuzzy frame to evaluate and resolve conflicts in Industry 4.0. The MADM model dwells on three primary points, i.e., (i) how to efficiently manage ambiguity and interrelationships in MADM issues; (ii) how to encompass the mindset of the decision maker in all areas concerned; and (iii) how to demonstrate results in terms of acceptance and rejection rather than ranking issues when more than one factor is involved. The test data of a fermatean fuzzy set (FFS) with rough relations, which addresses upper and lower approximations, demonstrates the possible uncertainty of the information. A fermatean fuzzy rough set (FFRS) is initially defined within the model. Subsequently, an FFRS incorporating the operator's confidence level is delineated. This demonstrates the importance of FFRS in MADM contexts and suggests that they require further examination of their data processing regulations. Furthermore, we evaluate the accuracy and validity of the results by employing mean absolute errors, cosine similarity of the operators, and Spearman rank correlation. To illustrate the accuracy and validity of our method in the MADM context, we performed a comparative analysis. Finally, a practical illustration of the selection of Industry 4.0 technologies within the healthcare sector exemplifies the efficacy and potential of this innovative approach for future applications of MADM. The intricate multi-stakeholder conflicts and data uncertainties presented by Industry 4.0 environments, especially regarding healthcare technology implementation, will be examined using the research framework illustrated in Fig. 1.

To address the complex challenges of green sustainable agriculture arising from regional economic growth, this study proposes a novel five-way decision (F-WD) architecture. The framework is evaluated using a Bayesian risk mechanism to minimize expected losses, leveraging the flexibility of q-rung orthopair fuzzy logic ( q ¨ ≥ 1 ) combined with outranking relations and decision-theoretic rough sets (DTRS). This approach defines alternatives across five semantically interpretable regions, extending the conventional three-way decision (T-WD) framework to provide a more granular and versatile classification system. Outranking relations enable partial preference modeling among alternatives by incorporating dominance and incomparability, offering nuanced judgments when criteria conflict or are incommensurable unlike traditional ranking methods. The study begins by formulating F-WD specific relative gain and loss functions, laying the groundwork for the five zone decision-making process. It further validates the reliability of threshold values, their properties, and their role in defining decision boundaries. Compared to conventional methods like TOPSIS, Fuzzy AHP, T-WD-DTRS, IVIFS-ELECTRE, and the proposed technique demonstrates superior decision granularity, interpretability, and classification accuracy. These results underscore the method's credibility, soundness, and practical effectiveness in handling uncertainty, hesitation, and compromise in risk-sensitive environments. The q-ROFS-based F-WD model not only outperforms existing approaches but also serves as a powerful, adaptive tool for sustainable development, strategic planning, and policymaking in complex scenarios.