A novel two-stage fuzzy classification method with different weight permutations for optimal GIS-based placement of wellness and sports centers

Abstract

The optimal placement of wellness and sports centers is critical to maximizing their accessibility, effectiveness, and impact on public health. Strategic location planning ensures that these facilities are conveniently accessible to the largest possible segment of the population, thereby encouraging higher participation rates. Accessibility is particularly crucial in urban areas where space is limited, and in rural or underserved regions where health and recreational services are often scarce. Moreover, the strategic placement of these centers can enhance community cohesion and stimulate local economies. This study develops a novel sorting Multi-Criteria Decision-Making (MCDM) method called fuzzy EDAS-Sort, a variant of the Evaluation based on Distance from Average Solution (EDAS) ranking method through a fuzzy sorting with different weight permutations to address the optimal placement of wellness and sports centers through assigning alternatives to predefined and ordered classes. It aims to identify the best locations for wellness and sports centers in Ardabil, Iran by employing the fuzzy EDAS-Sort method which is the main contribution of this research combined with Geographic Information Systems (GIS). By integrating fuzzy set theory with EDAS-Sort and GIS, the inherent uncertainties are handled in performance evaluation and spatial data analysis. According to the findings, the fuzzy EDAS-Sort is computationally efficient and provide highly accurate classification results for the optimal placement of wellness and sports centers. Numerical results demonstrate that 20% of the studied locations belonged to the “Excellent and optimal area” class, 33.3% to the “Good area” class, and 53.3% to the “Above average area” class. Finally, sensitivity analysis reveals that the proposed method is stable against weight variations, with less than 2.78% fluctuation in the classification results, ensuring a high degree of robustness.