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An Integrated Hesitant Fuzzy Linguistic MCDM Methods to Assess Smart City Solutions

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Decision Making Using AI in Energy and Sustainability

Part of the book series: Applied Innovation and Technology Management ((AITM))

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Abstract

The smart city concept is a new approach to urban planning that aims to connect different elements in a city to each other through technology and data to enhance municipal services and infrastructure, as well as citizens’ quality of life by the careful identification and selection of smart solutions. The aim of this study is to propose an analytical approach for the prioritization of suitable smart city solutions. In this context, a smart city model is presented with the help of the literature and industrial reports to define the needs of urban dwellers. The evaluation of the proposed model is considered a Multi Criteria Decision Making (MCDM) problem, which is addressed by the Hesitant Fuzzy Linguistic (HFL) approach. This HFL technique is used to deal with the uncertainty and hesitancy of expert opinions, and a Group Decision-Making (GDM) approach is employed to handle the partiality of the evaluation process. The importance degrees of the identified smart city parameters are calculated with the hierarchical HFL Simple Additive Weighting (SAW) method, whereas the smart city solutions are prioritized with the HFL Additive Ratio Assessment (ARAS) method. The applicability of the presented approach is demonstrated in a case study.

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Acknowledgements

The authors would like to express their gratitude to the experts who gave assistance, advice and feedback. This work has been supported by the Scientific Research Projects Commission of Galatasaray University (Project No: FOA-2021-1059 and FOA-2023-1181).

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Authors and Affiliations

  1. Galatasaray University, Industrial Engineering Department, Istanbul, Türkiye

    Gülçin Büyüközkan, Merve Güler & Esin Mukul

Authors
  1. Gülçin Büyüközkan
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  2. Merve Güler
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  3. Esin Mukul
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Corresponding author

Correspondence to Gülçin Büyüközkan .

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Editors and Affiliations

  1. Energy Institute, Istanbul Technical University, Maslak, Istanbul, Türkiye

    Gülgün Kayakutlu

  2. Energy Institute, Istanbul Technical University, Maslak, Istanbul, Türkiye

    M. Özgür Kayalica

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Büyüközkan, G., Güler, M., Mukul, E. (2023). An Integrated Hesitant Fuzzy Linguistic MCDM Methods to Assess Smart City Solutions. In: Kayakutlu, G., Kayalica, M.Ö. (eds) Decision Making Using AI in Energy and Sustainability. Applied Innovation and Technology Management. Springer, Cham. https://doi.org/10.1007/978-3-031-38387-8_13

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