Multi-Criteria Decision-Making for Urban Green Space Planning Using Intuitionistic Fuzzy AHP and Aggregation Operators

Resumen

Urban green spaces are essential components of smart cities, contributing to environmental sustainability, public health, and social well-being. Selecting optimal locations for green space development requires balancing multiple, often conflicting criteria such as environmental impact, accessibility, cost, and maintenance requirements. To address the inherent uncertainty and subjectivity in such multi-criteria decision-making (MCDM) problems, this study proposes a novel Intuitionistic Fuzzy Analytic Hierarchy Process (AHP) integrated with advanced aggregation operators, namely Frank aggregation operators, Power Geometric operators, and the Ordered Weighted Averaging (OWA) operator for robust criteria weighting and evaluation. The proposed framework combines Intuitionistic Fuzzy AHP to effectively capture experts’ hesitancy and partial membership, while the aggregation operators enhance the expressiveness and flexibility of criteria synthesis under fuzziness. Subsequently, the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to rank potential urban green space locations based on their relative closeness to the ideal solution. The Python-based experimental tool is utilized to implement and evaluate the proposed approach, ensuring reproducibility and computational efficiency. A case study conducted in a smart city context demonstrates superior performance and stability compared to conventional techniques.