This study aims to develop an optimized warehouse management systems (WMS) performance evaluation framework by addressing uncertainties such as inventory accuracy, demand variability, risk and resilience, regulatory changes, cost efficiency, environmental control, and technological advancement, ensuring operational efficiency and long-term sustainability.
The theory of circular intuitionistic fuzzy numbers (CIFNs) offers a comprehensive framework for identifying contradictory statements and streamlining content evaluation in multi-criteria decision-making (MCDM). The importance of criteria is objectively evaluated using the Method Based on the Removal Effects of Criteria (MEREC), while subjective weights are determined through the Step-Wise Weight Assessment Ratio Analysis (SWARA) technique. These objective and subjective weighting methods are useful for the importance and relevance of criteria in the decision-making process.
Alternative rankings are obtained using the AROMAN method with two-step normalization. The proposed MEREC–SWARA–AROMAN algorithm effectively supports WMS performance optimization by simplifying criteria selection and improving decision reliability.
The computational complexity increases with a large number of criteria and alternatives, limiting scalability for very large datasets. The framework primarily provides static evaluations and may not fully capture dynamic or time-dependent decision environments.
The framework provides decision-makers with a structured and transparent tool for evaluating WMS performance. It enhances the reliability of alternative ranking, leading to better resource allocation and operational planning. The model is flexible and can be adapted to other real-world decision problems.
The proposed MEREC–SWARA–AROMAN framework offers significant social benefits by enabling more transparent, inclusive, and equitable decision-making processes. By integrating both objective weighting (MEREC) and expert-driven subjective evaluation (SWARA), the model encourages stakeholder participation and reduces bias, thereby enhancing fairness and trust in decisions. Furthermore, its capability to incorporate social criteria—such as social responsibility, public welfare, and stakeholder satisfaction—supports sustainable development goals and improves societal outcomes.
Unlike prior studies focused on centralized systems, this research presents a real-world MCDM application for big data analysis under fuzziness and vagueness. The proposed framework demonstrates practical effectiveness in decision optimization, with applicability to sustainable energy projects supporting sustainable development.
