Optimizing Urban Planning and Development Using Pythagorean Fuzzy Sets in MCDM

Urbanization has brought about a notable change in cities that offers both chances and difficulties for urban development. To handle the complexity of urban planning and development, this work uses Pythagorean fuzzy sets (PFSs) inside Multi-criteria decision-making (MCDM) techniques. We present a new entropy metric for PFSs to improve the uncertainty-based decision-making precision. Considered under several economic, social environmental, and infrastructure criteria, the proposed framework evaluates urban regions for sustainable development using the complex proportional Assessment (COPRAS) approach. By means of case study choosing the ideal city for mixed urban development, our approach shows the superiority and effectiveness of PFSs entropy in urban planning uses. Based on weighted criteria, the results imply City is the best location for sustainable urban development. Comparative study with current entropy values confirms even more how well our method addresses the complexity of urban planning and supports inclusive urban development.