Hybrid Pythagorean Fuzzy Decision-Making Framework for Sustainable Urban Planning under Uncertainty

Sana Shahab¹, Vladimir Simic^2,*, Ashit Kumar Dutta^3,4, Mohd Anjum^5,*, Dragan Pamucar^6,7,8
1 Department of Business Administration, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
2 Sustainability Competence Centre, Széchenyi István University, Egyetem tér 1, Gyor, 9026, Hungary
3 Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Diriyah, Riyadh, 13713, Saudi Arabia
4 Research Center, Deanship of Scientific Research and Post-Graduate Studies, AlMaarefa University, Dariyah, Riyadh, 13713, Saudi Arabia
5 Department of Computer Engineering, Aligarh Muslim University, Aligarh, 202002, India
6 Department of Operations Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, Belgrade, 11000, Serbia
7 Faculty of Engineering, Dogus University, Umraniye, Istanbul, 34775, Türkiye
8 Department of Applied Mathematical Science, College of Science and Technology, Korea University, Sejong, 30019, Republic of Korea
* Corresponding Author: Vladimir Simic. Email: email ; Mohd Anjum. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.073945

Received 29 September 2025; Accepted 01 November 2025; Published online 19 December 2025

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Abstract

Environmental problems are intensifying due to the rapid growth of the population, industry, and urban infrastructure. This expansion has resulted in increased air and water pollution, intensified urban heat island effects, and greater runoff from parks and other green spaces. Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies. This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization (AAROM-TN), enhanced by a dual weighting strategy. The weighting approach integrates the Criteria Importance Through Intercriteria Correlation (CRITIC) method with the Criteria Importance through Means and Standard Deviation (CIMAS) technique. The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC, incorporate decision-makers’ preferences through CIMAS, and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets. A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework. A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results. Furthermore, a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.