Sustainable Urban Development: q-Rung Orthopair Fuzzy MCDM with Generalized Power Prioritized Yager Aggregation

Subramanian Petchimuthu; Balakrishnan Palpandi; K Rajakumar; Fathima Banu M

doi:10.31181/sor31202649

Authors

Subramanian Petchimuthu Department of Science and Humanities (Mathematics), University College of Engineering Nagercoil, Anna University, Tamil Nadu, India Author https://orcid.org/0000-0001-5633-7233
Balakrishnan Palpandi Department of Mathematics, University College of Engineering, Bharathidasan Institute of Technology Campus, Anna University, Tamil Nadu, India Author https://orcid.org/0000-0002-5769-6227
K Rajakumar Department of Mathematics, Dhanalakshmi Srinivasan University, Tiruchirapalli, Tamil Nadu, India Author https://orcid.org/0009-0004-8295-5827
Fathima Banu M Department of Science and Humanities (Mathematics), University College of Engineering Nagercoil, Anna University, Tamil Nadu, India Author https://orcid.org/0009-0008-6299-2734

DOI:

https://doi.org/10.31181/sor31202649

Keywords:

Sustainable Urban Development, Multi-Criteria Decision Making, q-Rung Orthopair Fuzzy Sets, Yager Aggregation Operators, Power Prioritized Aggregation

Abstract

In light of accelerating urbanization and the intensifying challenges of climate change, ensuring sustainability and resilience in urban environments has become a strategic imperative. This study introduces an innovative methodology that combines Multi-Criteria Decision Making (MCDM) with q-rung orthopair fuzzy Yager aggregation operators (q-ROFYAOs) to address the multifaceted complexities of sustainable urban development. The paper proposes novel Yager operations grounded in Yager t-norms within the framework of q-rung orthopair fuzzy sets (q-ROFSs). Utilizing these foundations, two advanced aggregation operators are formulated: the q-Rung Orthopair Fuzzy Generalized Power Prioritized Yager Weighted Average Operator (q-ROFGPOPRYWA) and the q-Rung Orthopair Fuzzy Generalized Power Prioritized Yager Weighted Geometric Operator (q-ROFGPOPRYWG). These operators satisfy essential mathematical properties, including idempotency, monotonicity, and boundedness, ensuring consistency and reliability in decision-making contexts. To demonstrate the practical relevance of the proposed approach, the operators are embedded within an MCDM framework and applied to a real-world case study on sustainable urban planning. The analysis encompasses sensitivity testing, comparative evaluations, and performance assessments, offering comprehensive insights into the robustness of the method. Finally, the paper provides a critical evaluation of the advantages and limitations of the proposed operators, underscoring their effectiveness in promoting urban resilience and minimizing environmental impact within complex decision-making environments.

Downloads

Download data is not yet available.

References

Peng, Z. R., Lu, K. F., Liu, Y., & Zhai, W. (2023). The pathway of urban planning AI: From planning support to plan-making. Journal of Planning Education and Research. https://doi.org/10.1177/0739456X231180568

Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91. https://doi.org/10.1016/j.cities.2019.01.032

Petchimuthu, S., Palpandi, B., & Senapati, T. (2024). Exploring pharmacological therapies through complex q-rung picture fuzzy Aczel–Alsina prioritized ordered operators in adverse drug reaction analysis. Engineering Applications of Artificial Intelligence, 133, 107996. https://doi.org/10.1016/j.engappai.2024.107996

Petchimuthu, S., & Palpandi, B. (2025). Sustainable urban innovation and resilience: Artificial intelligence and q-rung orthopair fuzzy expologarithmic framework. Spectrum of Decision Making and Applications, 2(1), 242–267.

Kamacı, H., & Petchimuthu, S. (2022). Soergel distance measures for q-rung orthopair fuzzy sets and their applications. In Q-rung orthopair fuzzy sets: Theory and applications (pp. 67–107). Springer. https://doi.org/10.1007/978-3-030-89721-1_3

Kamacı, H., Palpandi, B., & Petchimuthu, S. (2025). M-polar n-soft set and its application in multi-criteria decision-making. Computational and Applied Mathematics, 44, 64. https://doi.org/10.1007/s40314-024-02123-4

Seikh, M. R., & Mandal, U. (2022). Q-rung orthopair fuzzy Frank aggregation operators and their application in multiple attribute decision-making with unknown attribute weights. Granular Computing, 1–22. https://doi.org/10.1007/s41066-021-00290-2

Riaz, M., et al. (2020). A robust q-rung orthopair fuzzy information aggregation using Einstein operations with application to sustainable energy planning decision management. Energies, 13(9), 2155. https://doi.org/10.3390/en13092155

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

Atanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy Sets and Systems, 33(1), 37–45. https://doi.org/10.1016/0165-0114(89)90226-7

Seikh, M. R., & Mandal, U. (2023). Q-rung orthopair fuzzy Archimedean aggregation operators: Application in the site selection for software operating units. Symmetry, 15(9), 1680. https://doi.org/10.3390/sym15091680

Senapati, T., Simic, V., Saha, A., Dobrodolac, M., Rong, Y., & Tirkolaee, E. B. (2023). Intuitionistic fuzzy power Aczel-Alsina model for prioritization of sustainable transportation sharing practices. Engineering Applications of Artificial Intelligence, 119, 105716. https://doi.org/10.1016/j.engappai.2022.105716

Gohain, B., Chutia, R., & Dutta, P. (2022). Distance measure on intuitionistic fuzzy sets and its application in decision-making, pattern recognition, and clustering problems. International Journal of Intelligent Systems, 37(3), 2458–2501. https://doi.org/10.1002/int.22716

Ke, Y., Tang, H., Liu, M., & Qi, X. (2022). A hybrid decision-making framework for photovoltaic poverty alleviation project site selection under intuitionistic fuzzy environment. Energy Reports, 8, 8844–8856. https://doi.org/10.1016/j.egyr.2022.06.007

Wan, S., & Yi, Z. (2016). Power average of trapezoidal intuitionistic fuzzy numbers using strict t-norms and t-conorms. IEEE Transactions on Fuzzy Systems, 24(5), 1035–1047. https://doi.org/10.1109/TFUZZ.2015.2492543

Yager, R. R. (2013). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958–965. https://doi.org/10.1109/TFUZZ.2013.2241045

Akram, M., Dudek, W. A., & Dar, J. M. (2019). Pythagorean Dombi fuzzy aggregation operators with application in multicriteria decision-making. International Journal of Intelligent Systems, 34(11), 3000–3019. https://doi.org/10.1002/int.22180

Shahzadi, G., Akram, M., & Al-Kenani, A. N. (2020). Decision-making approach under Pythagorean fuzzy Yager weighted operators. Mathematics, 8(1), 70. https://doi.org/10.3390/math8010070

Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005

Wang, J., Wei, G., Wei, C., & Wei, Y. (2020). MABAC method for multiple attribute group decision making under q-rung orthopair fuzzy environment. Defence Technology, 16(1), 208–216. https://doi.org/10.1016/j.dt.2019.05.005

Wang, J., Zhang, R., Zhu, X., Zhou, Z., Shang, X., & Li, W. (2019). Some q-rung orthopair fuzzy Muirhead means with their application to multi-attribute group decision making. Journal of Intelligent and Fuzzy Systems, 36(2), 1599–1614. https://doi.org/10.3233/JIFS-181513

Wang, J., Wei, G., Lu, J., Alsaadi, F. E., Hayat, T., Wei, C., & Zhang, Y. (2019). Some q-rung orthopair fuzzy Hamy mean operators in multiple attribute decision-making and their application to enterprise resource planning systems selection. International Journal of Intelligent Systems, 34(10), 2429–2458. https://doi.org/10.1002/int.22183

Kausar, R., Farid, H. M. A., Riaz, M., & Bilgin, N. G. (2023). Innovative CODAS algorithm for q-rung orthopair fuzzy information and cancer risk assessment. Symmetry, 15(1), 205. https://doi.org/10.3390/sym15010205

Liu, P., & Liu, J. (2018). Some q-rung orthopair fuzzy Bonferroni mean operators and their application to multi-attribute group decision making. International Journal of Intelligent Systems, 33(2), 315–347. https://doi.org/10.1002/int.21940

Xing, Y., Zhang, R., Wang, J., Bai, K., & Xue, J. (2020). A new multi-criteria group decision-making approach based on q-rung orthopair fuzzy interaction Hamy mean operators. Neural Computing and Applications, 32, 7465–7488. https://doi.org/10.1007/s00521-019-04054-3

Yager, R. R. (2001). The power average operator. *IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 31*(6), 724–731. https://doi.org/10.1109/3468.983420

Yager, R. R. (2009). Prioritized OWA aggregation. Fuzzy Optimization and Decision Making, 8, 245–262. https://doi.org/10.1007/s10700-008-9047-0

Vargas-Hernández, J. G., Pallagst, K., & Zdunek-Wielgołaska, J. (2023). Urban green spaces as a component of an ecosystem. In Sustainable development and environmental stewardship: Global initiatives towards engaged sustainability (pp. 165–198). Springer International Publishing. https://doi.org/10.1007/978-3-030-87965-5_8

Khan, M. R., Wang, H., Ullah, K., & Karamti, H. (2022). Construction material selection by using multi-attribute decision making based on q-rung orthopair fuzzy Aczel–Alsina aggregation operators. Applied Sciences, 12(17), 8537. https://doi.org/10.3390/app12178537

Green, D., O’Donnell, E., Johnson, M., Slater, L., Thorne, C., Zheng, S., & Boothroyd, R. J. (2021). Green infrastructure: The future of urban flood risk management? Wiley Interdisciplinary Reviews: Water, 8(6), e1560. https://doi.org/10.1002/wat2.1560

Johnson, C., Matthews, T., Burke, M., & Jones, D. (2022). Acknowledgement of environmental concerns in transport infrastructure planning: A systematic review of the literature.

Shokoohyar, S. S., Jafari Gorizi, A., Ghomi, V., Liang, W., & Kim, H. J. (2022). Sustainable transportation in practice: A systematic quantitative review of case studies. Sustainability, 14(5), 2617. https://doi.org/10.3390/su14052617

Zhu, J., Xie, N., Cai, Z., Tang, W., & Chen, X. (2023). A comprehensive review of shared mobility for sustainable transportation systems. International Journal of Sustainable Transportation, 17(5), 527–551. https://doi.org/10.1080/15568318.2022.2059872

Cuthill, N., Cao, M., Liu, Y., Gao, X., & Zhang, Y. (2019). The association between urban public transport infrastructure and social equity and spatial accessibility within the urban environment: An investigation of Tramlink in London. Sustainability, 11(5), 1229. https://doi.org/10.3390/su11051229

Dubbeling, M., & De Zeeuw, H. (2011). Urban agriculture and climate change adaptation: Ensuring food security through adaptation. In *Resilient cities: Cities and adaptation to climate change-proceedings of the global forum 2010* (pp. 441–449). Springer Netherlands. https://doi.org/10.1007/978-94-007-0785-6_44

Korir, S. C., Rotich, J. K., & Mining, P. (2015). Urban agriculture and food security in developing countries: A case study of Eldoret Municipality, Kenya. International Journal of Agricultural Policy and Research, 3(4), 135–141. https://doi.org/10.15739/IJAPR.2015.013

Warren, E., Hawkesworth, S., & Knai, C. (2015). Investigating the association between urban agriculture and food security, dietary diversity, and nutritional status: A systematic literature review. Food Policy, 53, 54–66. https://doi.org/10.1016/j.foodpol.2015.03.001

Tapia, C., Randall, L., Wang, S., & Borges, L. A. (2021). Monitoring the contribution of urban agriculture to urban sustainability: An indicator-based framework. Sustainable Cities and Society, 74, 103130. https://doi.org/10.1016/j.scs.2021.103130

Adeyemo, R., & Kuhlmann, F. (2009). Resource use efficiency in urban agriculture in Southwestern Nigeria. Tropicultura, 27(1), 49–53. https://www.tropicultura.org/text/v27n1/49.pdf

Rong, Y., Li, Q., & Pei, Z. (2020). A novel q-rung orthopair fuzzy multi-attribute group decision-making approach based on Schweizer-Sklar operations and improved COPRAS method. Proceedings of the 4th International Conference on Computer Science and Application Engineering, 1–6.