Evaluation of the Smart Cities Listed in Smart City Index 2021 by Using Entropy Based Copras and Aras Methodology

• Autorzy: Gelmez Emel , Özceylan Eren

Identyfikatory

DOI

10.2478/fcds-2023-0007

• Języki publikacji: EN

Abstrakty

EN

Smart cities are included in the literature as a technology-based concept that has been on the agenda in recent years and whose framework is constantly changing with the changes in technology. There are different frameworks and indexes to define the smartness of a city. Smart City Index 2021 published by Institute for Management Development (IMD) and Singapore University of Technology and Design (SUTD) is one of the accepted studies in the world. In the report of Smart City Index 2021, 118 cities are evaluated in five criteria namely health & safety, mobility, activities, opportunities (work & school) and governance. To re-evaluate the cities and compare the results, a Multi-Criteria Decision Making (MCDM) process including Entropy based Complex Proportional Assessment (COPRAS) and Addivite Ratio Assessment (ARAS) methodology is applied in this paper. To prioritize the criteria, entropy weight method is used. 118 cities are ranked both technologically and structurally using the COPRAS and ARAS method. As a result of the analyses, according to these methods, the rankings of the smart cities are the same. Also, when technologically smart cities are listed, it is determined that the first three countries are Zhuhai, Shenzhen, Nanjing, and at the same time, Abu Dhabi, Chongqing, Hangzhou in terms of structurally.

Słowa kluczowe

EN

Smart City; multi-criteria decision making; entropy; COPRAS; ARAS; Smart City Index 2021

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2023
• Tom: Vol. 48, No. 2
• Strony: 153--180
• Opis fizyczny: Bibliogr. 62 poz., rys., tab.

Twórcy

autor

Gelmez Emel

• Department of Business Administration, Selçuk University, 42130 Konya, Turkey

• https://orcid.org/0000-0002-8774-607X

autor

Özceylan Eren

• Department of Industrial Engineering, Gaziantep University, 27100 Gaziantep, Turkey

• https://orcid.org/0000-0002-5213-6335

Bibliografia

• [1] Albino, V., Berardi, U., Dangelico, R. M., Smart cities: Definitions, dimensions, performance, and initiatives, Journal of Urban Technology, 22, 1, 2015, 3-21.
• [2] Prasad, D., & Alizadeh, T., What makes Indian cities smart? A policy analysis of smart cities mission, Telematics and Informatics, 55, 2020, 101466.
• [3] Hartawan, M. S., Putra, A. S., Muktiono, A., Smart City concept for integrated citizen information smart card or ICISC in DKI Jakarta, International Journal of Science, Technology & Management, 1, 4, 2020, 364-370.
• [4] Su, K., Li, J., Fu, H., Smart city and the applications, 2011 International Conference on Electronics, Communications and Control (ICECC), 2011, 1028-1031.
• [5] Dameri, R. P., Benevolo, C., Veglianti, E., & Li, Y., Understanding smart cities as a glocal strategy: A comparison between Italy and China, Technological Forecasting and Social Change, 142, 2019, 26-41.
• [6] Anand, A., Rufuss, D. D. W., Rajkumar, V., & Suganthi, L., Evaluation of sustainability indicators in smart cities for India using MCDM approach, Energy Procedia, 141, 2017, 211-215.
• [7] Vishnivetskaya, A., Alexandrova, E., “Smart city” concept. Implementation practice, IOP Conference Series: Materials Science and Engineering, 497, 2019, 012019.
• [8] Novotný, R., Kuchta, R., Kadlec, J., Smart city concept, applications and services, Journal of Telecommunications System & Management, 3, 2, 2014, 1-5.
• [9] Lombardi, P., Giordano, S., Farouh, H., Yousef, W., Modelling the smart city performance, Innovation: The European Journal of Social Science Research, 25, 2, 2012, 137-149.
• [10] SCI (2021), https://www.planbe.com.gr/news/smart-city-index-2021, Date of access: 24.01.2022.
• [11] Karabašević, D., Stanujkić, D., & Urošević, S., The MCDM Model for Personnel Selection Based on SWARA and ARAS Methods, Management (1820-0222), 20, 77, 2015.
• [12] Velasquez, M., Hester, P. T., An analysis of multi-criteria decision making methods, International Journal of Operations Research, 10, 2, 2013, 56-66.
• [13] Garg, H., Agarwal, N., Tripathi, A., Entropy based multi-criteria decision making method under fuzzy environment and unknown attribute weights, Global Journal of Technology and Optimization, 6, 3, 2015, 13-20.
• [14] Chen, C. H., A novel multi-criteria decision-making model for building material supplier selection based on entropy-AHP weighted TOPSIS, Entropy, 22, 2, 2020, 259.
• [15] Zavadskas, E., & Turskis, Z., A new additive ratio assesment (ARAS) method in multicriteria decision-making, Technological and Economic Development of Economy, 16, 2, 2010, 159-172.
• [16] Dashore, K., Pawar, S. S., Sohani, N., Verma, D. S., Product evaluation using entropy and multi criteria decision making methods, International Journal of Engineering Trend and Technology (IJETT), 4, 5, 2013, 183-2187.
• [17] Büyüközkan, G., & Güler, M., Smart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique, Measurement, 153, 107353.
• [18] Mulliner, E., Smallbone, K., & Maliene, V., An assessment of sustainable housing affordability using a multiple criteria decision making method, Omega, 41, 2, 2013, 270-279.
• [19] Stanković, J., Džunić, M., Džunić, Ž., & Marinković, S., A multi-criteria evaluation of the European cities’ smart performance: Economic, social and environmental aspects. Zbornik radova Ekonomskog fakulteta u Rijeci: časopis za ekonomsku teoriju i praksu, 35, 2, 2017, 519-550.
• [20] Shen, L., Huang, Z., Wong, S. W., Liao, S., Lou, Y., A holistic evaluation of smart city performance in the context of China, Journal of Cleaner Production, 200, 2018, 667-679.
• [21] Zhu, S., Li, D., Feng, H., Is smart city resilient? Evidence from China, Sustainable Cities and Society, 50, 2019, 101636.
• [22] Sojda, A., Smart city index based on topsis method, Zeszyty Naukowe. Organizacja i Zarządzanie/Politechnika Śląska, 148, 2020, 709-718.
• [23] Nasution, N., Bhawika, G. W., Wanto, A., Ginantra, N. L. W. S. R., Afriliansyah, T., Smart City Recommendations Using the TOPSIS Method, IOP Conference Series: Materials Science and Engineering, 846, 2020, 012028.
• [24] Ozkaya, G., & Erdin, C., Evaluation of smart and sustainable cities through a hybrid MCDM approach based on ANP and TOPSIS technique, Heliyon, 6, 10, 2020, e05052.
• [25] Ogrodnik, K., Multi-criteria analysis of smart cities in Poland, Geographia Polonica, 93, 2, 2020, 163-181.
• [26] Sotirelis, P., Nakopoulos, P., Valvi, T., Grigoroudis, E., Carayannis, E., Measuring smart city performance: A multiple criteria decision analysis approach, Journal of the Knowledge Economy, 2021, 1-29.
• [27] Koca, G., Egilmez, O., Akcakaya, O., Evaluation of the smart city: Applying the dematel technique, Telematics and Informatics, 62, 2021, 101625.
• [28] Hajduk, S., & Jelonek, D., A decision-making approach based on TOPSIS method for ranking smart cities in the context of urban energy, Energies, 14, 9, 2021, 2691.
• [29] Hajduk, S., Multi-Criteria Analysis of smart cities on the example of the Polish Cities, Resources, 10, 5, 2021, 44.
• [30] Ye, F., Chen, Y., Li, L., Li, Y., & Yin, Y., Multi-criteria decision-making models for smart city ranking: Evidence from the Pearl River Delta region, China, Cities, 128, 2022, 103793.
• [31] Popovic, G., Stanujkic, D., & Stojanovic, S., Investment project selection by applying COPRAS method and imprecise data, Serbian Journal of Management, 7, 2, 2012, 257-269.
• [32] Makhesana, M. A., Application of improved complex proportional assessment (COPRAS) method for rapid prototyping system selection, Rapid Prototyping Journal. 21, 6, 2015, 671-674.
• [33] Adali, E. A., & Işık, A. T., Air conditioner selection problem with COPRAS and ARAS methods. Manas Sosyal Araştırmalar Dergisi, 5, 2, 2016, 124-138.
• [34] Săraru, C. Ș., Selection of the optimum artificial lift method, on the Basis of ARAS, COPRAS and TOPSIS Models, Petroleum-Gas University of Ploiesti Bulletin, Technical Series, 68, 2, 2016.
• [35] Karabasevic, D., Paunkovic, J., & Stanujkic, D., Ranking of companies according to the indicators of corporate social responsibility based on SWARA and ARAS methods, Serbian Journal of Management, 11, 1, 2016, 43-53.
• [36] Singaravel, B., Shankar, D. P., & Prasanna, L., Application of MCDM method for the selection of optimum process parameters in turning process. Materials Today: Proceedings, 5, 5, 2018, 13464-13471.
• [37] Goswami, S. S., & Behera, D. K., Solving material handling equipment selection problems in an industry with the help of entropy integrated COPRAS and ARAS MCDM techniques, Process Integration and Optimization for Sustainability, 5, 4, 2021, 947-973.
• [38] Lalić, S., Jovičić, Ž., & Lukić, R., Application of the COPRAS Method in the evaluation of trade efficiency in Serbia, Economy and Market Communication Review, 22, 2, 2021, 497-509.
• [39] Gök-Kısa, A. C., Çelı̇k, P., & Peker, İ., Performance evaluation of privatized ports by entropy based TOPSIS and ARAS approach, Benchmarking: An International Journal, 29, 1, 2021, 18-135.
• [40] Nweze, S., & Achebo, J., Comparative enhancement of mild steel weld mechanical properties for better performance using COPRAS–ARAS Method, European Journal of Engineering and Technology Research, 6, 2, 2021, 70-74.
• [41] Gorgulu, Y., Ozceylan, E., & Ozkan, B., UI GreenMetric Ranking of Turkish Universities using Entropy Weight and COPRAS Methods. Proceedings of the International Conference on Industrial Engineering and Operations Management Bangalore, India, August 16-18, 2021, 1156-1165.
• [42] Karadağ Ak, Ö., Hazar, A., & Babuşcu, Ş., Evaluation of the financial performance of development and investment banks with entropy-based ARAS method, Macroeconomics and Finance in Emerging Market Economies, 2022, 1-21.
• [43] Karande, P., Zavadskas, E., & Chakraborty, S., A study on the ranking performance of some MCDM methods for industrial robot selection problems, International Journal of Industrial Engineering Computations, 7, 3, 2016, 399-422.
• [44] Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A., A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems, Materials & Design, 121, 2017, 237-253.
• [45] Aruldoss, M., Lakshmi, T. M., Venkatesan, V. P., A survey on multi criteria decision making methods and its applications, American Journal of Information Systems, 1, 1, 2013, 31-43.
• [46] Alao, M. A., Ayodele, T. R., Ogunjuyigbe, A. S. O., Popoola, O. M., Multi-criteria decision based waste to energy technology selection using entropy-weighted TOPSIS technique: The case study of Lagos, Nigeria, Energy, 201, 2020, 117675.
• [47] Bein, B., Entropy, Best Practice & Research Clinical Anaesthesiology, 20(1), 2006, 101-109.
• [48] Zhang, H., Gu, C. L., Gu, L. W., Zhang, Y., The evaluation of tourism destination competitiveness by TOPSIS & information entropy-A case in the Yangtze River Delta of China, Tourism Management, 32, 2, 2011, 443-451.
• [49] Hussain, S. A. I., Mandal, U. K., Entropy based MCDM approach for selection of material, National Level Conference on Engineering Problems and Application of Mathematics, 2016, 1-6.
• [50] Gandotra, N., Kizielewicz, B., Anand, A., Bączkiewicz, A., Shekhovtsov, A., Wątróbski, J., Rezaei, A., Sałabun, W., New pythagorean entropy measure with application in multi-criteria decision analysis, Entropy, 23, 12, 1600.
• [51] Sahoo, S., Choudhury, B., Optimal selection of an electric power wheelchair using an integrated COPRAS and EDAS approach based on Entropy weighting technique, Decision Science Letters, 11, 1, 2022, 21-34.
• [52] Vaid, S. K., Vaid, G., Kaur, S., Kumar, R., Sidhu, M. S., Application of multi-criteria decision-making theory with VIKOR-WASPAS-Entropy methods: A case study of silent Genset, Materials Today: Proceedings, 50, 2022, 2416-2423.
• [53] Kildienė, S., Kaklauskas, A., Zavadskas, E. K., COPRAS based comparative analysis of the European country management capabilities within the construction sector in the time of crisis, Journal of Business Economics and Management, 12, 2, 2011, 417-434.
• [54] Zavadskas, E. K., Kaklauskas, A., Vilutiene, T., Multicriteria evaluation of apartment blocks maintenance contractors: Lithuanian case study, International Journal of Strategic Property Management, 13, 4, 2009, 319-338.
• [55] Stanojkovic, J., Radovanovic, M., Selection of drill for drilling with high pressure coolant using entropy and copras MCDM method, UPB Sci. Bull. Ser. D Mech. Eng., 79, 4, 2017, 199-204.
• [56] Yazdani, M., Jahan, A., Zavadskas, E., Analysis in material selection: Influence of normalization tools on COPRAS-G, Economic Computation & Economic Cybernetics Studies & Research, 51, 1, 2017, 59-74.
• [57] Chatterjee, P., Athawale, V. M., Chakraborty, S., Materials selection using complex proportional assessment and evaluation of mixed data methods, Materials & Design, 32, 2, 2011, 851-860.
• [58] Hezer, S., Gelmez, E., Özceylan, E., Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment, Journal of Infection and Public Health, 14, 6, 2021, 775-786.
• [59] Liu, N., & Xu, Z., An overview of ARAS method: Theory development, application extension, and future challenge. International Journal of Intelligent Systems, 36, 7, 2021, 3524-3565.
• [60] Sivalingam, V., Kumar, P. G., Prabakaran, R., Sun, J., Velraj, R., & Kim, S. C., An automotive radiator with multi-walled carbon-based nanofluids: A study on heat transfer optimization using MCDM techniques, Case Studies in Thermal Engineering, 29, 2022, 101724.
• [61] Dahooie, H., J., Kazimieras Zavadskas, E., Abolhasani, M., Vanaki, A., & Turskis, Z., A novel approach for evaluation of projects using an interval-valued fuzzy additive ratio assessment (ARAS) method: a case study of oil and gas well drilling projects, Symmetry, 10, 2, 2019, 45.
• [62] Sliogeriene, J., Turskis, Z., & Streimikiene, D., Analysis and choice of energy generation technologies: The multiple criteria assessment on the case study of Lithuania, Energy Procedia, 32, 2013, 11-20.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).