<p>Traditional Data Envelopment Analysis models, although widely used to measure efficiency across sectors, have several limitations that restrict their use in real-world settings, especially in the dynamic, complex environment of retail chain stores. This study introduces a novel range-adjusted robust Data Envelopment Analysis (DEA) model designed to evaluate the efficiency of retail chain stores, focusing specifically on economic, productivity, social, and environmental factors. By incorporating fuzzy programming techniques, the method effectively manages the uncertainties inherent in retail operations. The model is applied to assess the performance of 13 selected branches of the Shahrvand chain store in Tehran, providing valuable insights into their operational effectiveness and highlighting areas for potential improvement. The inclusion of discrete scenarios enhances the model’s realism, addressing the limitations of traditional DEA techniques that often depend on deterministic data. The results offer practical insights for practitioners and policymakers, helping to identify high-performing stores and the factors behind their success. This knowledge can guide targeted strategies to boost the efficiency of underperforming units through better resource allocation, streamlined operations, and sustainable practices. Additionally, the study advances the existing literature by utilizing advanced analytical methods within the retail sector, offering a solid framework for efficiency evaluation and benchmarking amid uncertainty. The results indicate that keeping high-high quadrants are difficult, emphasizing the multi-objective framework justification. However, it is worth noting that data collection limitations, especially across different regions, may require the development of additional efficiency KPIs tailored to specific contexts.</p>