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

Abstract

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.

Keywords

• 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

Abstract

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.

Keywords

• 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

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