TEREDDÜTLÜ BULANIK DİLSEL ÇOK KRİTERLİ KARAR VERME ÇERÇEVESİNİ KULLANARAK AKILLI ŞEHİRLERİN ÖNCELİKLENDİRİLMESİ

Öz

Smart cities represent contemporary urbanization paradigms aimed at enhancing efficiency, sustainability, and livability through technology and data-driven solutions. Positioned as a cornerstone for a more equitable and sustainable future, smart cities address the pressing challenges of growing urban populations with innovative approaches. However, evaluating their performance requires comprehensive analytical methodologies capable of managing uncertainty and conflicting priorities. This study proposes an integrated hesitant fuzzy linguistic (HFL) multi-criteria decision-making framework to address this need. The methodology combines the HFL Analytic Hierarchy Process (AHP) for determining the relative importance of evaluation criteria and the HFL Evaluation Based on Distance from Average Solution (EDAS) for ranking smart cities. By embracing the flexibility and hesitancy in decision-makers’ judgments, this framework ensures robust and reliable results even under uncertain conditions. The proposed approach is applied to assess and rank smart cities, with Aalborg, Denmark, emerging as the top-performing city. Aalborg's exemplary achievements in sustainable and safe transport systems, pollution control, and environmental protection underscore its leadership in smart city initiatives. This study contributes to the field by providing a scalable and adaptable decision-support tool for policymakers and urban planners, paving the way for more effective smart city performance evaluation.

Anahtar Kelimeler

• Hesitant Fuzzy Linguistic Term Set
• Analytic Hierarchy Process (AHP)
• Evaluation Based on Distance from Average Solution (EDAS)
• Multi-Criteria Decision-Making
• Smart City
• Performance Evaluation

PRIORITIZING SMART CITIES USING HESITANT FUZZY LINGUISTIC MULTI-CRITERIA DECISION-MAKING FRAMEWORK

Öz

Smart cities represent contemporary urbanization paradigms aimed at enhancing efficiency, sustainability, and livability through technology and data-driven solutions. Positioned as a cornerstone for a more equitable and sustainable futu re, smart cities address the pressing challenges of growing urban populations with innovative approaches. However, evaluating their performance requires comprehensive analytical methodologies capable of managing uncertainty and conflicting priorities. This study proposes an integrated hesitant fuzzy linguistic (HFL) multi-criteria decision-making framework to address this need. The methodology combines the HFL Analytic Hierarchy Process (AHP) for determining the relative importance of evaluation criteria and the HFL Evaluation Based on Distance from Average Solution (EDAS) for ranking smart cities. By embracing the flexibility and hesitancy in decision-makers’ judgments, this framework ensures robust and reliable results even under uncertain conditions. The proposed approach is applied to assess and rank smart cities, with Aalborg, Denmark, emerging as the top-performing city. Aalborg's exemplary achievements in sustainable and safe transport systems, pollution control, and environmental protection underscore its leadership in smart city initiatives. This study contributes to the field by providing a scalable and adaptable decision-support tool for policymakers and urban planners, paving the way for more effective smart city performance evaluation.

Anahtar Kelimeler

• Hesitant Fuzzy Linguistic Term Set
• Analytic Hierarchy Process (AHP)
• Evaluation Based on Distance from Average Solution (EDAS)
• Multi-Criteria Decision-Making
• Smart City
• Performance Evaluation

Kaynakça

1. Abu-Rayash, A., and Dincer, I. (2023) “Development and Application of an Integrated Smart City Model”, Heliyon, 9(4).
2. Alabdulatif, A., Khalil, I., Kumarage, H., Zomaya, A. Y., and Yi, X. (2019) “Privacy-preserving Anomaly Detection in the Cloud for Quality Assured Decision-Making in Smart Cities”, Journal of Parallel and Distributed Computing, 127, 209-223.
3. Alghamdi, M. (2023) “Smart City Urban Planning Using an Evolutionary Deep Learning Model”, Soft Computing, 1-13.
4. Anand, A., Rufuss, D. D. W., Rajkumar, V., and Suganthi, L. (2017) “Evaluation of Sustainability Indicators in Smart Cities for India Using MCDM Approach”, Energy Procedia, 141, 211-215.
5. Andrade, R. O., and Yoo, S. G. (2019) “A Comprehensive Study of the Use of Lora in the Development of Smart Cities”, Applied Sciences, 9(22), 4753.
6. Anthopoulos, L., and Fitsilis, P. (2015) “Social Networks in Smart Cities: Comparing Evaluation Models”, In 2015 IEEE First International Smart Cities Conference (ISC2), 1-6. IEEE.
7. Anthopoulos, L., and Giannakidis, G. (2016) “Policy Making in Smart Cities: Standardizing City's Energy Efficiency with Task-Based Modelling”, Journal of ICT Standardization, 4(2), 111-146.
8. ASCIMER, (2017) “Assessment Methodology for Smart City Projects: Application to the Mediterranean Region”, European Investment Bank Institute, 81.

9. Ayağ, Z. (2022) “An Intelligent Approach to Evaluating CAD Software Packages Through Hesitant Fuzzy AHP”, Journal of Advanced Manufacturing Systems, 21(02), 317-335.
10. Ayağ, Z., and Samanlioglu, F. (2021) “A Hesitant Fuzzy Linguistic Terms Set-Based AHP-TOPSIS Approach to Evaluate ERP Software Packages”, International Journal of Intelligent Computing and Cybernetics, 14(1), 54-77.
11. Bagheri, R., Nezhad, M.Z., Rizi, M.H.P, and Sadri, M. (2023) “Identifying and Evaluating Factors Affecting User Privacy in the Smart City Using the Meta-Synthesis Method and the Fuzzy Dematel Technique”, International Journal of Information Technology and Decision Making, 1-32.
12. Baran, M., Kłos, M., Chodorek, M., and Marchlewska-Patyk, K. (2022) “The Resilient Smart City Model–Proposal for Polish Cities”, Energies, 15(5), 1818.
13. Barba-Romero, S., and Pomerol, J. C. (2000) “Multicriterion Decision in Management: Principles and Practice”, Operations Research Management Science, 25.
14. Büyüközkan, G., and Güler, M. (2021) “A Combined Hesitant Fuzzy MCDM Approach for Supply Chain Analytics Tool Evaluation”, Applied Soft Computing, 112, 107812.
15. Büyüközkan, G., Karabulut, Y., and Mukul, E. (2018) “A Novel Renewable Energy Selection Model for United Nations' Sustainable Development Goals”, Energy, 165, 290-302.
16. Büyüközkan, G., Mukul, E., and Kongar, E. (2021) “Health Tourism Strategy Selection Via SWOT Analysis and Integrated Hesitant Fuzzy Linguistic AHP-MABAC Approach”, Socio-Economic Planning Sciences, 74, 100929.
17. Carli, R., Dotoli, M., and Pellegrino, R. (2018) “A Decision-Making Tool for Energy Efficiency Optimization of Street Lighting”, Computers & Operations Research, 96, 223-235.
18. Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., ... and Scholl, H. J. (2012) “Understanding Smart Cities: An Integrative Framework”, In 2012 45th Hawaii International Conference on System Sciences, 2289-2297, IEEE.
19. Coban, V. (2020) “Solar Energy Plant Project Selection with AHP Decision-Making Method Based on Hesitant Fuzzy Linguistic Evaluation”, Complex & Intelligent Systems, 6, 507-529.
20. Cocchia, A. (2014) “Smart and Digital City: A Systematic Literature Review”, Smart City: How to Create Public and Economic Value with High Technology in Urban Space, 13-43.
21. Cohen, B. (2013) “Smart City Wheel”, Retrieved from Smart & Safe City, URL: http://www. smartcircle. org/smartcity/blog/boyd-cohen-the-smart-city-wheel.
22. Colak, M., and Kaya, İ. (2020) “Multi-criteria Evaluation of Energy Storage Technologies Based on Hesitant Fuzzy Information: A Case Study for Turkey”, Journal of Energy Storage, 28, 101211.
23. Crawford, G. B. (1987) “The Geometric Mean Procedure for Estimating the Scale of a Judgement Matrix”, Mathematical Modelling, 9(3-5), 327-334.
24. da Silva, J. G. (2023) “Guidelines for a Participatory Smart City Model to Address Amazon’s Urban Environmental Problems”, PeerJ Computer Science, 9, e1694.
25. Bruni, E., Panza, A., Sarto, L., and Khayatian, F. (2017) “Evaluation of Cities’ Smartness by Means of Indicators for Small and Medium Cities and Communities: A Methodology for Northern Italy”, Sustainable Cities and Society, 34, 193-202.
26. Deloitte Report, (2015) “Smart Cities-How Rapid Advances in Technology Are Reshaping Our Economy and Society”.
27. Di Bella, E., Corsi, M., and Leporatti, L. (2015) “A Multi-Indicator Approach for Smart Security Policy Making”, Social Indicators Research, 122, 653-675.
28. Easypark (2017) “Smart City Index”, URL: https://easyparkgroup.com/smart-cities-index/. Ecer, F. (2018) “Third-party Logistics (3pls) Provider Selection Via Fuzzy AHP and EDAS Integrated Model”, Technological and Economic Development of Economy, 24(2), 615-634.
29. El Hamdani, S., Benamar, N., and Younis, M. (2020) “A Protocol for Pedestrian Crossing and Increased Vehicular Flow in Smart Cities”, Journal of Intelligent Transportation Systems, 24(5), 514-533.
30. Escolar, S., Villanueva, F. J., Santofimia, M. J., Villa, D., del Toro, X., and López, J. C. (2019) “A Multiple-Attribute Decision Making-based Approach for Smart City Rankings Design”, Technological Forecasting and Social Change, 142, 42-55.
31. Ezugwu, A. E., Hashem, I. A. T., Oyelade, O. N., Almutari, M., Al-Garadi, M. A., Abdullahi, I. N., ... and Chiroma, H. (2021) “A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating Covid-19”, BioMed Research International, 2021.
32. Feng, X., Wei, C., and Liu, Q. (2018) “EDAS Method for Extended Hesitant Fuzzy Linguistic Multi-Criteria Decision Making”, International Journal of Fuzzy Systems, 20, 2470-2483.
33. Fernandez-Anez, V., Fernández-Güell, J. M., and Giffinger, R. (2018) “Smart City Implementation and Discourses: An Integrated Conceptual Model. The Case of Vienna”, Cities, 78, 4-16.
34. Forrester. (2010) “Helping CIOs Understand "Smart City" Initiatives”, Growth, 17(2), 1-17.
35. Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Turskis, Z., and Antucheviciene, J. (2017) “A New Multi-Criteria Model Based on Interval Type-2 Fuzzy Sets and EDAS Method for Supplier Evaluation and Order Allocation with Environmental Considerations”, Computers & Industrial Engineering, 112, 156-174.
36. Ghorabaee, M. K., Zavadskas, E. K., Amiri, M., and Turskis, Z. (2016) “Extended EDAS Method for Fuzzy Multi-Criteria Decision-Making: An Application to Supplier Selection”, International Journal of Computers Communications & Control, 11(3), 358-371.
37. Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., and Turskis, Z. (2015) “Multi-criteria Inventory Classification Using A New Method of Evaluation Based on Distance from Average Solution (EDAS)”, Informatica, 26(3), 435-451.
38. Giang, T. T. H., Camargo, M., Dupont, L., and Mayer, F. (2017) “A Review of Methods for Modelling Shared Decision-Making Process in a Smart City Living Lab”, In 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), 189-194, IEEE.
39. Giffinger, R., and Pichler-Milanović, N. (2007) “Smart Cities: Ranking of European Medium-Sized Cities”, Centre of Regional Science, Vienna University of Technology.
40. Golden, B. L., and Wang, Q. (1989) “An Alternate Measure of Consistency”, The Analytic Hierarchy Process: Applications and Studies, 68-81.
41. Goumiri, S., Yahiaoui, S., and Djahel, S. (2023) “Smart Mobility in Smart Cities: Emerging challenges, recent advances and future directions”, Journal of Intelligent Transportation Systems, 1-37.
42. Hanine, M., Boutkhoum, O., El Barakaz, F., Lachgar, M., Assad, N., Rustam, F., Ashraf, I. (2021) “An Intuitionistic Fuzzy Approach for Smart City Development Evaluation for Developing Countries: Moroccan Context”, Mathematics, 9(21), 2668.
43. Hollands, R. G. (2008) “Will the Real Smart City Please Stand Up? Intelligent, Progressive or Entrepreneurial?”, City, 12(3), 303-320.
44. Hsieh, H. N., Hou, C. Y., and Chia, P. C. (2011, July) “A Study of Smart Town Development Strategies”, In Multimedia Technology (ICMT), 2011 International Conference on, 6684-6689.
45. Hwang, C. L., Yoon, K., Hwang, C. L., and Yoon, K. (1981) “Methods for Multiple Attribute Decision Making”, Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey, 58-191.
46. Ibrahim, H. A., Qahtan, S., Zaidan, A. A., Deveci, M., Hajiaghaei-Keshteli, M., Mohammed, R. T., and Alamoodi, A. H. (2024) “Sustainability in Mobility for Autonomous Vehicles Over Smart City Evaluation; Using Interval-Valued Fermatean Fuzzy Rough Set-Based Decision-Making Model”, Engineering Applications of Artificial Intelligence, 129, 107609.
47. IESE Business School, University of Navarra (2016) “Center for Globalization and Strategy”, IESE Cities in Motion Index.
48. Ivaldi, E., Penco, L., Isola, G., and Musso, E. (2020) “Smart Sustainable Cities and the Urban Knowledge-Based Economy: A NUTS3 Level Analysis”, Social Indicators Research, 150(1), 45-72.
49. Jain, B., Brar, G., Malhotra, J., and Rani, S. (2017) “A Novel Approach for Smart Cities in Convergence to Wireless Sensor Networks”, Sustainable Cities and Society, 35, 440-448.
50. Kahraman, C., Keshavarz Ghorabaee, M., Zavadskas, E. K., Cevik Onar, S., Yazdani, M., and Oztaysi, B. (2017) “Intuitionistic Fuzzy EDAS Method: An Application to Solid Waste Disposal Site Selection”, Journal of Environmental Engineering and Landscape Management, 25(1), 1-12.
51. Karaşan, A., and Kahraman, C. (2018) “A Novel Interval-Valued Neutrosophic EDAS Method: Prioritization of the United Nations National Sustainable Development Goals”, Soft Computing, 22, 4891-4906.
52. Kociuba, D., Sagan, M., and Kociuba, W. (2023) “Toward the Smart City Ecosystem Model”, Energies, 16(6), 2795. Kollarova, M., Granak, T., Strelcova, S., and Ristvej, J. (2023) “Conceptual Model of Key Aspects of Security and Privacy Protection in a Smart City in Slovakia”, Sustainability, 15(8), 6926.
53. Korea Agency for Infrastructure Technology Advancement. (2023) “Smart City Top Agenda - Urban Competitiveness through Digital Transition and Climate Action”, Smart City Global Journal. KPMG. (2021) “The Futures of Cities”, Industrial Report.
54. Krishankumar, R., Pamucar, D., Cavallaro, F., and Ravichandran, K. S. (2022a) “Clean Energy Selection for Sustainable Development by Using Entropy-Based Decision Model with Hesitant Fuzzy Information”, Environmental Science and Pollution Research, 29(28), 42973-42990.
55. Krishankumar, R., Pamucar, D., Deveci, M., Aggarwal, M., and Ravichandran, K. S. (2022b) “Assessment of Renewable Energy Sources for Smart Cities’ Demand Satisfaction Using Multi-Hesitant Fuzzy Linguistic Based Choquet Integral Approach”, Renewable Energy, 189, 1428-1442.
56. Krishankumar, R., Ravichandran, K. S., Kar, S., Gupta, P., and Mehlawat, M. K. (2021) “Double-Hierarchy Hesitant Fuzzy Linguistic Term Set-Based Decision Framework for Multi-Attribute Group Decision-Making”, Soft Computing, 25, 2665-2685.
57. Kumar, A., Akhtar, M. A. K., and Pandey, A. (2022) “Design of Internet of Things (IoT) System Based Smart City Model on Raspberry Pi”, IETE Journal of Research, 1-8.
58. Kumar, R., Khan, A. I., Abushark, Y. B., Alam, M. M., Agrawal, A., and Khan, R. A. (2020) “A Knowledge-Based Integrated System of Hesitant Fuzzy Set, AHP and TOPSIS for Evaluating Security-Durability of Web Applications”, IEEE Access, 8, 48870-48885.
59. Kutlu Gündoğdu, F., Kahraman, C., and Civan, H. N. (2018) “A Novel Hesitant Fuzzy EDAS Method and Its Application to Hospital Selection”, Journal of Intelligent & Fuzzy Systems, 35(6), 6353-6365.
60. Lafioune, N., and St-Jacques, M. (2020) “Towards the Creation of a Searchable 3D Smart City Model”, Innovation & Management Review, 17(3), 285-305.
61. Lazaroiu, G. C., and Roscia, M. (2012) “Definition Methodology for the Smart Cities Model”, Energy, 47(1), 326-332. Lee, K. L., and Lin, S. C. (2008) “A Fuzzy Quantified SWOT Procedure for Environmental Evaluation of an International Distribution Center”, Information Sciences, 178(2), 531-549.
62. Liu, H., and Rodríguez, R. M. (2014) “A Fuzzy Envelope for Hesitant Fuzzy Linguistic Term Set and Its Application to Multicriteria Decision Making”, Information Sciences, 258, 220-238.
63. Liu, P., Wang, H., and Wei, G. (2023) “EDAS Method for Multi-Attribute Decision-Making with Generalized Hesitant Fuzzy Numbers and Its Application to Energy Projects Selection”, Journal of Intelligent & Fuzzy Systems, 45(2), 2763-2779.
64. Lom, M., and Pribyl, O. (2021) “Smart City Model Based on Systems Theory”, International Journal of Information Management, 56, 102092.
65. Lombardi, P., Giordano, S., Farouh, H., and Yousef, W. (2012) “Modelling the Smart City Performance”, Innovation: The European Journal of Social Science Research, 25(2), 137-149.
66. Makki, A. A., and Alqahtani, A. Y. (2024) “Analysis of the Barriers to Smart City Development Using DEMATEL”, Urban Science, 8(1), 10.
67. Manupati, V. K., Ramkumar, M., and Samanta, D. (2018) “A Multi-Criteria Decision-Making Approach for the Urban Renewal in Southern India”, Sustainable Cities and Society, 42, 471-481.
68. Mattoni, B., Gugliermetti, F., and Bisegna, F. (2015) “A Multilevel Method to Assess and Design the Renovation and Integration of Smart Cities”, Sustainable Cities and Society, 15, 105-119.
69. Mi, X., Wu, X., Tang, M., Liao, H., Al-Barakati, A., Altalhi, A. H., and Herrera, F. (2019) “Hesitant Fuzzy Linguistic Analytic Hierarchical Process with Prioritization, Consistency Checking, and Inconsistency Repairing”, IEEE Access, 7, 44135-44149.
70. Mi, Y. (2023) “Evaluating Social Media and Internet Addiction Using FAHP and EDAS Techniques”, Soft Computing, 1-15.
71. Milošević, M. R., Milošević, D. M., Stanojević, A. D., Stević, D. M., Simjanović, D. J. (2021) “Fuzzy and Interval AHP Approaches in Sustainable Management for the Architectural Heritage in Smart Cities”, Mathematics, 9(4), 304.
72. Milošević, M. R., Milošević, D. M., Stević, D. M., and Stanojević, A. D. (2019) “Smart City: Modeling Key Indicators in Serbia Using IT2FS”, Sustainability, 11(13), 3536.
73. Ministry of Land, Infrastructure and Transport (2019) “Korea’s Smart City Policy & Strategies”.
74. Ministry of Urban Development Government of India (2015) “Smart City Mission statement & Guidelines”.
75. Tavakkoli-Moghaddam, R., and Mousavi, M. (2015) “Group Decision Making Based on a New Evaluation Method and Hesitant Fuzzy Setting with an Application to an Energy Planning Problem”, International Journal of Engineering, 28(9), 1303-1311.
76. Mousavi, S. M., Gitinavard, H., and Siadat, A. (2014) “A New Hesitant Fuzzy Analytical Hierarchy Process Method for Decision-Making Problems Under Uncertainty”, In 2014 IEEE International Conference on Industrial Engineering and Engineering Management, 622-626, IEEE.
77. Nabeeh, N. A., Abdel-Basset, M., El-Ghareeb, H. A., and Aboelfetouh, A. (2019) “Neutrosophic Multi-Criteria Decision-Making Approach for Iot-Based Enterprises”, IEEE Access, 7, 59559-59574.
78. Neirotti, P., De Marco, A., Cagliano, A. C., Mangano, G., and Scorrano, F. (2014) “Current Trends in Smart City Initiatives: Some Stylised Facts”,Cities, 38, 25-36.
79. OECD. (2020) “Building on the Outcomes of the 1st OECD Roundtable on Smart Cities and Inclusive Growth”. Ogrodnik, K. (2020) “Multi-Criteria Analysis of Smart Cities in Poland”, Geographia Polonica, 93(2), 163-181.
80. Ohta, R., Salomon, V. A., and Silva, M. B. (2020) “Classical, Fuzzy, Hesitant Fuzzy and Intuitionistic Fuzzy Analytic Hierarchy Processes Applied to Industrial Maintenance Management”, Journal of Intelligent & Fuzzy Systems, 38(1), 601-608.
81. Onar, S. Ç., Büyüközkan, G., Öztayşi, B., and Kahraman, C. (2016) “A New Hesitant Fuzzy QFD Approach: An Application to Computer Workstation Selection”, Applied Soft Computing, 46, 1-16.
82. Pamučar, D., and Ćirović, G. (2015) “The selection of Transport and Handling Resources in Logistics Centers Using Multi-Attributive Border Approximation Area Comparison (MABAC)”, Expert Systems with Applications, 42(6), 3016-3028.
83. Peng, X., and Dai, J. (2017) “Hesitant Fuzzy Soft Decision-Making Methods Based on WASPAS, MABAC And COPRAS with Combined Weights”, Journal of Intelligent and Fuzzy Systems, 33(2), 1313-1325.
84. Peng, X., and Liu, C. (2017) “Algorithms for Neutrosophic Soft Decision Making Based on EDAS, New Similarity Measure and Level Soft Set”, Journal of Intelligent & Fuzzy Systems, 32(1), 955-968.
85. Pereira, G. R. B., Guimarães, L. G. D. A., Cimon, Y., Da Silva Barreto, L. K., and Hermann Nodari, C. (2023) “Conceptual Model for Assessing Logistics Maturity in Smart City Dimensions”, Administrative Sciences, 13(4), 114.
86. Public Technology Platform. (2016) “URL: http://www.kamuteknolojiplatformu.org/index.php, 2016”.
87. Rădulescu, C. M., Slava, S., Rădulescu, A. T., Toader, R., Toader, D. C., and Boca, G. D. (2020) “A Pattern of Collaborative Networking for Enhancing Sustainability of Smart Cities”, Sustainability, 12(3), 1042.
88. Rani, R., and Potika, K. (2024) “Smart City Wildfire Risk Analysis with Fuzzy Multi-Criteria Decision-Making”, International Journal of Semantic Computing, 18(3).
89. Rodriguez, R. M., Martinez, L., and Herrera, F. (2011) “Hesitant Fuzzy Linguistic Term Sets for Decision Making”, IEEE Transactions on Fuzzy Systems, 20(1), 109-119.
90. Rondini, A., Lagorio, A., Pezzotta, G., and Pinto, R. (2017) “Adopting a Multi Criteria Decision Method for the Introduction of Psss in the Smart City Context”, Summer School Francesco Turco. Proceedings, 355-361.
91. Saaty, T. L. (1980) “The Analytic Hierarchy Process (AHP)”, The Journal of the Operational Research Society, 41(11), 1073-1076.
92. Shao, Q. G., Jiang, C. C., Lo, H. W., and Liou, J. J. (2023) “Establishing a Sustainable Development Assessment Framework for a Smart City Using a Hybrid Z-Fuzzy-Based Decision-Making Approach”, Clean Technologies and Environmental Policy, 25(9), 3027-3044.
93. Sharma, K., and Tayal, S. (2019) “Indian Smart City Ranking Model Using Taxicab Distance-Based Approach”, Energy Systems, 1-18.
94. Shen, L., Huang, Z., Wong, S. W., Liao, S., and Lou, Y. (2018) “A Holistic Evaluation of Smart City Performance in the Context of China”, Journal of Cleaner Production, 200, 667-679.
95. Shi, H., Tsai, S. B., Lin, X., and Zhang, T. (2017) “How to Evaluate Smart Cities’ Construction? A Comparison of Chinese Smart City Evaluation Methods Based on PSF”, Sustainability, 10(1), 37.
96. Shinde, N., and Kiran, P. S. (2016) “A Survey of Cloud Auction Mechanisms & Decision Making in Cloud Market to Achieve Highest Resource & Cost Efficiency”, In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques, 1158-1162, IEEE.
97. Singh, K., Kolar, P., Nanduri, S., Seetha Ram, V., and Kumar, D. (2024) “CSR and Smart City Progress: a Proposed Model for Urban Development”, Letters in Spatial and Resource Sciences, 17(1), 3.
98. Stević, Ž., Vasiljević, M., Puška, A., Tanackov, I., Junevičius, R., and Vesković, S. (2019) “Evaluation of Suppliers Under Uncertainty: A Multiphase Approach Based on Fuzzy AHP and Fuzzy EDAS”, Transport, 34(1), 52-66.
99. Takeda E. (1993) “A Note on Consistent Adjustments of Pairwise Comparison Judgments, Mathematical and Computer Modelling”, 17, 29–35.
100. Tariq, M. A. U. R., Faumatu, A., Hussein, M., Shahid, M. L. U. R., and Muttil, N. (2020) “Smart City-Ranking of Major Australian Cities to Achieve a Smarter Future”, Sustainability, 12(7), 2797.
101. Torra, V. (2010) “Hesitant Fuzzy Sets”, International Journal of Intelligent Systems, 25(6), 529-539.
102. Tüysüz, F., and Şimşek, B. (2017) “A Hesitant Fuzzy Linguistic Term Sets-Based AHP Approach for Analyzing the Performance Evaluation Factors: An Application to Cargo Sector”, Complex & Intelligent Systems, 3, 167-175.
103. Uçar, A., Şemşit, S., and Negiz, N. (2017) “Avrupa Birliği Akıllı Kent Uygulamaları ve Türkiye'deki Yansımalari (European Union Urban Intelligent Applications and Reflections in Turkey)”, Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 22.
104. United Nations Commission on Science and Technology for Development (2016) “Smart Cities and Infrastructure”, Hungary.
105. Urban Hub. (2017) “How Wearable Technologies Are Connecting People to Smart Cities”, URL: http://www.urban-hub.com/technology/how-wearable-technologies-are-connecting-people-to-smart-cities/.
106. Varol, Ç. (2017) “Sürdürülebilir Gelişmede Akıllı Kent Yaklaşımı: Ankara’daki Belediyelerin Uygulamaları (Smart City Approach in Sustainable Development: Applications of Municipalities in Ankara)”, Çağdaş Yerel Yönetimler, 26(1), 43-58.
107. Vasuaninchita, M., Vongmanee, V., and Rattanawong, W. (2020) “The Novel Paradigm of Economics Driven for Local Smart Sustain Cities Modeling Using Exploratory Factor Analysis and Planning Technique Using Fuzzy Evaluation Decision Making”, Sustainability, 12(3), 793.
108. Vlahogianni, E. I., Kepaptsoglou, K., Tsetsos, V., and Karlaftis, M. G. (2016) “A Real-Time Parking Prediction System for Smart Cities”, Journal of Intelligent Transportation Systems, 20(2), 192-204.
109. Wang, P., Wang, J., and Wei, G. (2019) “EDAS Method for Multiple Criteria Group Decision Making Under 2-Tuple Linguistic Neutrosophic Environment”, Journal of Intelligent & Fuzzy Systems, 37(2), 1597-1608.
110. Xu, X., Liu, X., Xu, Z., Dai, F., Zhang, X., and Qi, L. (2019) “Trust-oriented IoT Service Placement for Smart Cities in Edge Computing”, IEEE Internet of Things Journal, 7(5), 4084-4091.
111. Xu, Z. (2014) “Hesitant Fuzzy Sets Theory”, 314, Cham: Springer International Publishing.
112. Ye, F., Chen, Y., Li, L., Li, Y., Yin, Y. (2022) “Multi-criteria Decision-Making Models for Smart City Ranking: Evidence from the Pearl River Delta Region”, China. Cities, 128, 103793.
113. Yenkar, P. P., and Sawarkar, S. D. (2023) “A Novel Ensemble Approach Based on MCC and MCDM Methods for Prioritizing Tweets Mentioning Urban Issues in Smart City”, Kybernetes, 52(9), 3613-3646.
114. Yilanci, V., Candan, G., and Shah, M. I. (2023) “Identifying the Roles of Energy and Economic Factors on Environmental Degradation in MINT Economies: A Hesitant Fuzzy Analytic Hierarchy Process”, Environmental Science and Pollution Research, 30(19), 55768-55781.
115. Zavadskas, E. K., Kaklauskas, A., and Sarka, V. (1994) “The New Method of Multicriteria Complex Proportional Assessment of Projects”, Technological and Economic Development of Economy, 1(3), 131-139.
116. Zhu, B., and Xu, Z. (2014) “Analytic Hierarchy Process-Hesitant Group Decision Making”, European Journal of Operational Research, 239(3), 794-801.
117. Zhu, B., Xu, Z., Zhang, R., and Hong, M. (2016) “Hesitant Analytic Hierarchy Process”, European Journal of Operational Research, 250(2), 602-614.
118. Zhu, S., Li, D., and Feng, H. (2019) “Is Smart City Resilient? Evidence from China”, Sustainable Cities and Society, 50, 101636.