Comparative Investigation of Normalization Techniques and Their Influence on MCDM Ranking – A Case Study

Nikola Petrović; Vesna Jovanović; Marijana Petrović; Boban Nikolić; Jelena Mihajlović

doi:10.31181/smeor21202542

Authors

Nikola Petrović University of Nis, Faculty of Mechanical Engineering, 18000 Niš, Serbia Author https://orcid.org/0000-0002-9166-1263
Vesna Jovanović University of Nis, Faculty of Mechanical Engineering, 18000 Niš, Serbia Author https://orcid.org/0000-0001-9252-7894
Marijana Petrović University of Belgrade, Faculty of Transport and Traffic Engineering, 11000 Belgrade, Serbia Author https://orcid.org/0000-0001-9173-9238
Boban Nikolić University of Nis, Faculty of Mechanical Engineering, 18000 Niš, Serbia Author https://orcid.org/0000-0002-9694-6719
Jelena Mihajlović University of Nis, Faculty of Mechanical Engineering, 18000 Niš, Serbia Author https://orcid.org/0000-0002-9787-0226

DOI:

https://doi.org/10.31181/smeor21202542

Keywords:

Normalization, CRITIC, TOPSIS, Alternative drive technologies, Fuels

Abstract

Urban traffic significantly contributes to air pollution, and its effects are expected to increase in the coming years. Environmental policy measures can help achieve long-term goals, in which new modes of mobility for residents can play a significant role. However, local-level decision-making is also crucial. In the face of economic volatility, cities must carefully decide on sustainable transportation expenditures that respect budget limitations and promote local air quality standards. To create a sustainable traffic strategy and address the rise in traffic demands due to increased passenger transport, the use of buses with alternative drive technologies and fuel options is becoming increasingly important in European cities. However, buses utilizing alternative drive technologies and fuel options have varying impacts on air pollution, and the required investments and expenses vary. To determine the appropriate propulsion technology and fuel for buses specifically in Nis, the analysis based on multiple criteria will be conducted using the CRITIC (Criteria Importance Through Intercriteria Correlation) and in the following TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) methods according to the defined criteria. The CRITIC method utilizes different normalization types to compute weighting coefficients and analyze their effect on the alternative rankings. Additionally, Spearman's rank correlation coefficient will be used to examine the degree of correlation between the rankings of different alternative drive technologies and fuel options for buses, calculated through the TOPSIS method.

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References

Petrović, N., Bojović, N., Marinković, D., Jovanović, V., & Milanović, S. (2023). A two-phase model for the evaluation of urbanization impacts on carbon dioxide emissions from transport in the European Union. Tehnički Vjesnik, 30(2), 514–520. https://doi.org/10.17559/TV-20221018103946

Ng, K.-W., & Tong, H.-Y. (2024). Comparisons of driving characteristics between electric and diesel-powered bus operations along identical bus routes. Sustainability, 16(12), 4950. https://doi.org/10.3390/su16124950

European Commission. (n.d.). Clean transport. Retrieved January 10, 2025, from https://transport.ec.europa.eu/transport-themes/clean-transport_en

European Union. (2009). *Regulation (EC) No 595/2009 of the European Parliament and of the Council of 18 June 2009*. Official Journal of the European Union. Retrieved December 29, 2024, from https://eur-lex.europa.eu/eli/reg/2009/595/oj/eng

Petrović, N., Mihajlović, J., Jovanović, V., Ćirić, D., & Živojinović, T. (2023). Evaluating annual operation performance of Serbian railway system by using multiple criteria decision-making technique. Acta Polytechnica Hungarica, 20(1), 157–168. https://doi.org/10.12700/APH.20.1.2023.20.11

Tadić, D., Lukić, J., Komatina, N., Marinković, D., & Pamučar, D. (2025). A fuzzy decision-making approach to electric vehicle evaluation and ranking. Tehnički Vjesnik / Technical Gazette. Accepted for publishing.

Hamurcu, M., & Eren, T. (2022). Applications of the MOORA and TOPSIS methods for decision of electric vehicles in public transportation technology. Transport, 37(4), 251–263. https://doi.org/10.3846/transport.2022.17783

Petrović, N., Marković, S., Nikolić, B., Jovanović, V., & Petrović, M. (2024). Evaluating alternative propulsion systems for urban public transport in Niš: A multicriteria decision-making approach. Journal of Engineering Management and Systems Engineering, 3(2), 72–81. https://doi.org/10.56578/jemse030202

Sahoo, S. K., Choudhury, B. B., & Dhal, P. R. (2024). A bibliometric analysis of material selection using MCDM methods: Trends and insights. Spectrum of Mechanical Engineering and Operational Research, 1(1), 189–205. https://doi.org/10.31181/smeor11202417

Tešić, D., Božanić, D., & Milić, A. (2023). A multi-criteria decision-making model for pontoon bridge selection: An application of the DIBR II-NWBM-FF MAIRCA approach. Journal of Engineering Management and Systems Engineering, 2(4), 212–223. https://doi.org/10.56578/jemse020403

Komatina, N., Marinković, D., Tadić, D., & Pamučar, D. (2025). Advancing PFMEA decision-making: FRADAR based prioritization of failure modes using AP, RPN, and multi-attribute assessment in the automotive industry. Tehnički Glasnik / Technical Journal, 19(3).

Milani, A. S., Shanian, A., Madoliat, R., & Nemes, J. A. (2005). The effect of normalization norms in multiple attribute decision making models: A case study in gear material selection. Structural and Multidisciplinary Optimization, 29(4), 312–318. https://doi.org/10.1007/s00158-004-0473-1

Jahan, A., Bahraminasab, M., & Edwards, K. L. (2012). A target-based normalization technique for materials selection. Materials & Design, 35, 647–654. https://doi.org/10.1016/j.matdes.2011.09.005

Jahan, A., Mustapha, F., Sapuan, S. M., Ismail, M. Y., & Bahraminasab, M. (2012). A framework for weighting of criteria in ranking stage of material selection process. The International Journal of Advanced Manufacturing Technology, 58(1), 411–420. https://doi.org/10.1007/s00170-011-3366-7

Baydaş, M., Yılmaz, M., Jović, Ž., Stević, Ž., Özuyar, S. E. G., & Özçil, A. (2024). A comprehensive MCDM assessment for economic data: Success analysis of maximum normalization, CODAS, and fuzzy approaches. Financial Innovation, 10, 105. https://doi.org/10.1186/s40854-023-00588-x

Baydaş, M., Eren, T., & İyibildiren, M. (2023). Normalization technique selection for MCDM methods: A flexible and conjunctural solution that can adapt to changes in financial data types. Necmettin Erbakan Üniversitesi Siyasal Bilgiler Fakültesi Dergisi, 5(Special Issue), 148–164. https://doi.org/10.51124/jneusbf.2023.54

Ersoy, N. (2022). Normalization procedures for COCOSO method: A comparative analysis under different scenarios. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 22(2), 217–234. https://doi.org/10.24889/ifede.974252

Aytekin, A. (2021). Comparative analysis of the normalization techniques in the context of MCDM problems. Decision Making: Applications in Management and Engineering, 4(2), 1–25. https://doi.org/10.31181/dmame210402001a

Puška, A., Štilić, A., Pamučar, D., Božanić, D., & Nedeljković, M. (2024). Introducing a novel multi-criteria ranking of alternatives with weights of criterion (RAWEC) model. MethodsX, 12, 102628. https://doi.org/10.1016/j.mex.2024.102628

Petrović, N., Jovanović, V., Marković, S., Marinković, D., & Petrović, M. (2024). Multicriteria sustainability assessment of transport modes: A European Union case study for 2020. *Journal of Green Economics and Low-Carbon Development, 3*(1), 36–44. https://doi.org/10.56578/jgelcd030104

Trung, D. D., Truong, N. X., Duc, D. V., & Bao, N. C. (2024). Data normalization in RAWEC method: Limitations and remedies. Yugoslav Journal of Operations Research. Advance online publication. https://doi.org/10.2298/YJOR240315020T

Žižović, M., Miljković, B., & Marinković, D. (2020). Objective methods for determining criteria weight coefficients: A modification of the CRITIC method. Decision Making: Applications in Management and Engineering, 3(2), 149–161. https://doi.org/10.31181/dmame2003149z

Pamučar, D., Žižović, M., & Đuričić, D. (2022). Modification of the CRITIC method using fuzzy rough numbers. Decision Making: Applications in Management and Engineering, 5(2), 362–371. https://doi.org/10.31181/dmame0316102022p

Çelen, A. (2014). Comparative analysis of normalization procedures in TOPSIS method: With an application to Turkish deposit banking market. Informatica, 25(2), 185–208. https://doi.org/10.15388/Informatica.2014.10

Ersoy, N. (2022). The influence of statistical normalization techniques on performance ranking results: The application of MCDM method proposed by Biswas and Saha. International Journal of Business Analytics (IJBAN), 9(5), 1–21. https://doi.org/10.4018/IJBAN.298017

Arsu, T., & Ayçin, E. (2021). Evaluation of OECD countries with multi-criteria decision-making methods in terms of economic, social and environmental aspects. Operational Research in Engineering Sciences: Theory and Applications, 4(2), 55–78. https://doi.org/10.31181/oresta20402055a

Milićević, M. R., & Župac, G. Ž. (2012). Objektivni pristup određivanju težina kriterijuma [Objective approach to determining criteria weights]. Vojnotehnički Glasnik, 60(1), 39–56. https://doi.org/10.5937/vojtehg1201039M

Dimitrijević, B. (2017). Višeatributivno odlučivanje [Multi-attribute decision making]. University of Belgrade – Faculty of Transport and Traffic Engineering.

Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer Verlag.

Petrović, N., Živojinović, T., & Mihajlović, J. (2023). Evaluating the annual operational efficiency of passenger and freight road transport in Serbia through entropy and TOPSIS methods. Journal of Engineering Management and Systems Engineering, 2(4), 204–211. https://doi.org/10.56578/jemse020402

Parashar, S., Bhattacharya, S., Titiyal, R., & Roy, D. G. (2024). Assessing environmental performance of service supply chain using fuzzy TOPSIS method. Health Services and Outcomes Research Methodology, 24, 46–72. https://doi.org/10.1007/s10742-023-00303-4

Lin, H. (2024). Light pollution evaluation research based on the entropy weight method combined with the TOPSIS model. Transactions on Environment, Energy and Earth Sciences, 3, 514–522. https://doi.org/10.62051/c5cbhj73

CIVITAS. (2013). Policy note: Smart choices for cities – Clean buses for your city. https://civitas.eu/sites/default/files/civ_pol-an_web.pdf

Nikolić, B., Kegl, B., Milanović, S., Petrović, N., & Marković, S. (2023). Characteristics of biodiesel as a fuel for diesel engines. Innovative Mechanical Engineering, 2(3). http://ime.masfak.ni.ac.rs/Dokumenta/papers/v2n3/v2n3-6_Nikolic_et_al.pdf

Kousoulidou, M., Fontaras, G., Mellios, G., & Ntziachristos, L. (2008). Effect of biodiesel and bioethanol on exhaust emissions (Report No. 08.RE.0006.V1). Aristotle University Thessaloniki, Laboratory of Applied Thermodynamics.

Rutz, D., & Janssen, R. (2008). Biofuel technology handbook. WIP Renewable Energies.

Birath, K., & Sjölin, L. (2007). Clean vehicles and alternative fuels: Trends and visions. NICHES Consortium.

Kruchina, V. (2023). The possibility of electrification in public transport bus services. Acta Technica Jaurinensis, 16(4), 158–166. https://doi.org/10.14513/actatechjaur.00713

European Commission. (2001–2006). Clean Urban Transport for Europe (CUTE) project. https://trimis.ec.europa.eu/project/clean-urban-transport-europe

Fuel Cell Buses. (n.d.). Retrieved December 20, 2024, from https://fuelcellbuses.eu/

CIVITAS. (2016). Policy note: Smart choices for cities – Alternative fuel buses. https://civitas.eu/sites/default/files/civ_pol-08_m_web.pdf

Petrović, N. (2018). Managing the impacts of urbanization and transport modes on the environment quality [Doctoral dissertation, University of Belgrade, Faculty of Transport and Traffic Engineering].

Damjanović, M., Stević, Ž., Stanimirović, D., Tanackov, I., & Marinković, D. (2022). Impact of the number of vehicles on traffic safety: Multiphase modeling. Facta Universitatis, Series: Mechanical Engineering, 20(1), 177–197. https://doi.org/10.22190/FUME220215012D

Puška, A., Stojanović, I., & Štilić, A. (2023). The influence of objective weight determination methods on electric vehicle selection in urban logistics. Journal of Intelligent Management and Decision, 2(3), 117–129. https://doi.org/10.56578/jimd020302

Mukhametzyanov, I. (2021). Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering, 4(2), 76–105. https://doi.org/10.31181/dmame210402076i

Petrović, N., Jovanović, V., Petrović, M., Nikolić, B., & Pavlović, J. (2022). Evaluating the operation performance of the Serbian transport freight system by using multiple criteria decision-making technique. Engineering Today, 1(4), 33–40. https://doi.org/10.5937/engtoday2204033P

Wang, C.-N., Le, T. Q., Chang, K.-H., & Dang, T.-T. (2022). Measuring road transport sustainability using MCDM-based entropy objective weighting method. Symmetry, 14(5), 1033. https://doi.org/10.3390/sym14051033

Conover, W. J. (1980). Practical nonparametric statistics (2nd ed.). Wiley.

Gopal, K. K. (2006). 100 statistical tests (3rd ed.). SAGE Publications.