Electric Vehicle Selection Using the Technique of Precise Order Preference
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Conventional vehicles rely on petrol and its derivatives to deliver strong performance and extended range. However, these vehicles significantly contribute to environmental pollution and the depletion of fossil fuels. Consequently, alternative-fuelled vehicle technologies have been developed, leading to a paradigm shift in the transportation sector toward electric vehicles (EVs) that reduce greenhouse gas emissions and lessen their environmental impact. For potential EV buyers, a selection problem exists due to the diverse range of alternatives. This study proposes an integrated model that selects and ranks EVs based on three main criteria: cost, driving performance and technical features. Hierarchical best worst method (HBWM), full consistency method (FUCOM), and step-wise weight assessment ratio analysis (SWARA) methods are employed to determine the importance weights for the main criteria and their sub-criteria. A sample EV set consisting of 10 potential alternatives was created for the study, and these EVs were ranked by combining the weights of the main criteria and sub-criteria obtained using the three aforementioned methods employing the technique of precise order preference (TPOP) method. Our experiments suggest that the most critical criterion among the 10 sub-criteria was price, and the least important criterion was high speed.
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Feng J, Xu SX, Li M. A novel multi-criteria decision-making method for selecting the site of an electric-vehicle charging station from a sustainable perspective. Sustainable Cities and Society. 2021;65(102623):1-20. DOI: 10.1016/j.scs.2020.102623.
Khan F, Ali Y, Khan A. Sustainable hybrid electric vehicle selection in the context of a developing country. Air Quality, Atmosphere & Health. 2020;13(4):489-499. DOI: 10.1007/s11869-020-00812-y.
Ghosh A, et al. Application of hexagonal fuzzy MCDM methodology for site selection of electric vehicle charging station. Mathematics. 2021;9(4):1-27. DOI: 10.3390/math9040393.
Wang R, et al. Study on location decision framework of electric vehicle battery swapping station: Using a hybrid MCDM method. Sustainable Cities and Society. 2020;61(102149):1-12. DOI: 10.1016/j.scs.2020.102149.
Ecer F. A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews. 2021;143(110916):1-19. DOI: 10.1016/j.rser.2021.110916.
Biswas TK, Das MC. Selection of commercially available electric vehicle using fuzzy AHP-MABAC. Journal of the Institution of Engineers (India): Series C. 2019;100(3):531-537. DOI: 10.1007/s40032-018-0481-3.
Von Rosenstiel DP, Heuermann DF, Hüsig S. Why has the introduction of natural gas vehicles failed in Germany? —Lessons on the role of market failure in markets for alternative fuel vehicles. Energy Policy. 2015;78(1):91-101. DOI: 10.1016/j.enpol.2014.12.022.
Safarzadeh S, Khansefid S, Rasti-Barzoki M. A group multi-criteria decision-making based on best-worst method. Computers & Industrial Engineering. 2018;126(1):111-121. DOI: 10.1016/j.cie.2018.09.011.
Liang X, Wu X, Liao H. A gained and lost dominance score II method for modelling group uncertainty: Case study of site selection of electric vehicle charging stations. Journal of Cleaner Production. 2020;262(121239):1-9. DOI: 10.1016/j.jclepro.2020.121239.
Abdel-Basset M, Mohamed R. A novel plithogenic TOPSIS-CRITIC model for sustainable supply chain risk management. Journal of Cleaner Production. 2020;247(119586):1-15. DOI: 10.1016/j.jclepro.2019.119586.
Ali A, Rashid T. Hesitant fuzzy best‐worst multi‐criteria decision‐making method and its applications. International Journal of Intelligent Systems. 2019;34(8):1953-1967. DOI: 10.1002/int.22131.
Golui S, Mahapatra BS, Mahapatra GS. A new correlation-based measure on Fermatean fuzzy applied on multi-criteria decision making for electric vehicle selection. Expert Systems with Applications. 2024;237(121605):1-13. DOI: 10.1016/j.eswa.2023.121605.
Więckowski J, et al. Complex sensitivity analysis in Multi-Criteria Decision Analysis: An application to the selection of an electric car. Journal of Cleaner Production. 2023;390(136051):1-15. DOI: 10.1016/j.jclepro.2023.136051.
Tian ZP, et al. Data-driven multi-criteria decision support method for electric vehicle selection. Computers & Industrial Engineering. 2023;177(109061):1-14. DOI: 10.1016/j.cie.2023.109061.
Bošković S, et al. An alternative ranking order method accounting for two-step normalization (AROMAN) – A case study of the electric vehicle selection problem. IEEE Access. 2023;11(1):1-12. DOI: 10.1109/ACCESS.2023.3265818.
Ziemba P. Selection of electric vehicles for the needs of sustainable transport under conditions of uncertainty—a comparative study on fuzzy MCDA methods. Energies. 2021;14(22):1-25. DOI: 10.3390/en14227786.
Sonar HC, Kulkarni SD. An integrated AHP-MABAC approach for electric vehicle selection. Research in Transportation Business & Management. 2021;41(100665):1-8. DOI: 10.1016/j.rtbm.2021.100665.
Sejwal R, et al. Selection of electric vehicles using MCDM techniques. In: Ranganath MS, Pavan KK. (eds.) Advanced Production and Industrial Engineering, Amsterdam, Netherlands: IOS Press; 2022. p. 598-607.
Ren X, Sun S, Yuan R. A study on selection strategies for battery electric vehicles based on sentiments, analysis, and the MCDM model. Mathematical Problems in Engineering. 2021;1(9984343):1-23. DOI: 10.1155/2021/9984343.
Arslan R, Bircan H. Çok kriterli karar verme teknikleriyle elde edilen sonuçların Copeland yöntemiyle birleştirilmesi ve karşılaştırılması. Yönetim ve Ekonomi Dergisi. 2020;27(1):109-127. DOI: 10.18657/yonveek.540125.
Görçün ÖF, et al. Electric vehicle selection for industrial users using an interval-valued intuitionistic fuzzy COPRAS-based model. Environment, Development and Sustainability. 2024;2024(1):1-42. DOI: 10.1007/s10668-024-05562-w.
Pradhan P, Shabbiruddin, Pradhan S. Selection of electric vehicle using integrated Fuzzy-MCDM approach with analysis on challenges faced in hilly terrain. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2022;44(2):2651-2673. DOI: 10.1080/15567036.2022.2056665.
Zhao H, Hao X. Location decision of electric vehicle charging station based on a novel grey correlation comprehensive evaluation multi-criteria decision method. Energy. 2024;299(131356):1-16. DOI: 10.1016/j.energy.2024.131356.
Chumbi WE, et al. Suitable site selection of public charging stations: a fuzzy TOPSIS MCDA framework on capacity substation assessment. Energies. 2024;17(14):1-27. DOI: 10.3390/en17143452.
Mhana KH, Awad HA. An ideal location selection of electric vehicle charging stations: Employment of integrated analytical hierarchy process with geographical information system. Sustainable Cities and Society. 2024;107(105456):1-19. DOI: 10.1016/j.scs.2024.105456.
Gomes CFS, et al. Multicriteria methodology for selection of a personal electric vehicle. Brazilian Journal of Operations & Production Management. 2023;20(2):415-1415. DOI: 10.14488/BJOPM.1415.2023.
Althaqafi T. Cultivating sustainable supply chain practises in electric vehicle manufacturing: A MCDM Approach to assessing GSCM performance. World Electric Vehicle Journal. 2023;14(10):1-18. DOI: 10.3390/wevj14100290.
Wei Q, Zhou C. A multi-criteria decision-making framework for electric vehicle supplier selection of government agencies and public bodies in China. Environmental Science and Pollution Research. 2023;30(4):10540-10559. DOI: 10.1007/s11356-022-22783-6.
Deveci M, Simic V, Torkayesh AE. Remanufacturing facility location for automotive Lithium-ion batteries: An integrated neutrosophic decision-making model. Journal of Cleaner Production. 2021;317(128438):1-20. DOI: 10.1016/j.jclepro.2021.128438.
Abdali H, Sahebi H, Pishvaee M. The water-energy-food-land nexus at the sugarcane-to-bioenergy supply chain: A sustainable network design model. Computers & Chemical Engineering. 2021;145(107199):1-19. DOI: 10.1016/j.compchemeng.2020.107199.
Loganathan MK, et al. Multi-Criteria decision making (MCDM) for the selection of Li-Ion batteries used in electric vehicles (EVs). Materials Today: Proceedings. 2021;41(1):1073-1077. DOI: 10.1016/j.matpr.2020.07.179.
Zhang L, et al. A multi-criteria decision method for performance evaluation of public charging service quality. Energy. 2020;195(116958):1-13. DOI: 10.1016/j.energy.2020.116958.
Samaie F, et al. Comparison of sustainability models in development of electric vehicles in Tehran using fuzzy TOPSIS method. Sustainable Cities and Society. 2020;53(101912):1-13. DOI: 10.1016/j.scs.2019.101912.
Nunic Z, Staevic Z. A Novel Integrated Multi-Criteria Decision-Making Model: FUCOM-EDAS-M. Innovation as An Initiator of The Development Conference 2019, 7-10 Dec. 2019, Belgrade, Serbia. p. 19-34.
Rani P, Mishra AR. Fermatean fuzzy Einstein aggregation operators-based MULTIMOORA method for electric vehicle charging station selection. Expert Systems with Applications. 2021;182(115267):1-23. DOI: 10.1016/j.eswa.2021.115267.
Yagmahan B, Yılmaz H. An integrated ranking approach based on group multi-criteria decision making and sensitivity analysis to evaluate charging stations under sustainability. Environment, Development and Sustainability. 2023;25(1):96-121. DOI: 10.1007/s10668-021-02044-1.
Sałabun W, Karczmarczyk A. Using the comet method in the sustainable city transport problem: an empirical study of the electric powered cars. Procedia Computer Science. 2018;126(1):2248-2260. DOI: 10.1016/j.procs.2018.07.224.
Zavadskas EK, Turskis Z. A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy. 2010; 16(2):159-172. DOI: 10.3846/tede.2010.10.
Yıldız A, et al. Best-worst yöntemi ile otomat makinası ürün dağıtımındaki risklerin değerlendirilmesi. El-Cezeri. 2022;9(4):1215-1227. DOI: 10.31202/ecjse.1132087.
Rezaei J. Best-worst multi-criteria decision-making method. Omega. 2015;53(1):49-57. DOI: 10.1016/j.omega.2014.11.009.
Pamučar D, Stević Ž, Sremac S. A new model for determining weight coefficients of criteria in MCDM models: Full consistency method (FUCOM). Symmetry. 2018;10(1):393: 1-22. DOI: 10.3390/sym10090393.
Keršuliene V, Zavadskas EK, Turskis Z. Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management. 2010;11(2):243-258.
Sarwar A, Zafar A, Qadir A. Analysis and prioritization of risk factors in the management of Halal supply chain management. Discover Sustainability. 2021;2(1):1-10. DOI: 10.1007/s43621-021-00039-6.
Hasan MG, Ashraf Z, Khan MF. Multi‐choice best‐worst multi‐criteria decision‐making method and its applications. International Journal of Intelligent Systems. 2022;37(2):1129-1156. DOI: 10.1002/int.22663.
Rezaei J, Wang J, Tavasszy L. Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications. 2015;42(23):9152-9164. DOI: 10.1016/j.eswa.2015.07.073.
Sadjadi S, Karimi M. Best-worst multi-criteria decision-making method: A robust approach. Decision Science Letters. 2018; 7(4):323-340. DOI: 10.5267/j.dsl.2018.3.003.
Guo S, Qi Z. A fuzzy best-worst multi-criteria group decision-making method. IEEE Access. 2021;9(1):118941-118952. DOI: 10.1109/ACCESS.2021.3106296.
Wan Ahmad WNK, et al. Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. Journal of Cleaner Production. 2017;153(1):242-252. DOI: 10.1016/j.jclepro.2017.03.166.
Gupta H, Barua MK. Identifying enablers of technological innovation for Indian MSMEs using best–worst multi criteria decision making method. Technological Forecasting and Social Change. 2016;107(1):69-79. DOI: 10.1016/j.techfore.2016.03.028.
Mohtashami A. A novel modified fuzzy best-worst multi-criteria decision-making method. Expert Systems with Applications. 2021;181(115196):1-10. DOI: 10.1016/j.eswa.2021.115196.
Zhou T, et al. Selecting industrial IoT Platform for digital servitisation: a framework integrating platform leverage practices and cloud HBWM-TOPSIS approach. International Journal of Production Research. 2023; 61(12):4022-4044. DOI: 10.1080/00207543.2021.2002458.
Tabatabaei MH, et al. Hierarchical decision-making using a new mathematical model based on the best-worst method. International Journal of Computers Communications & Control. 2019;14(6):710-725. DOI: 10.15837/ijccc.2019.6.3675.
Nemati A, Zolfani SH, Khazaelpour P. A novel gray FUCOM method and its application for better video games experiences. Expert Systems with Applications. 2023;234(121041):1-20. DOI: 10.1016/j.eswa.2023.121041.
Ruzgys A, et al. Integrated evaluation of external wall insulation in residential buildings using SWARA-TODIM MCDM method. Journal of Civil Engineering and Management. 2014;20(1):103-110. DOI: 10.3846/13923730.2013.843585.
Bairagi B, et al. A De Novo multi-approaches multi-criteria decision-making technique with an application in performance evaluation of material handling device. Computers & Industrial Engineering. 2015;87(1):267-282. DOI: 10.1016/j.cie.2015.05.015.
Mutlu NG, Altuntas S. Technique of precise order preference for multiple risk assessment in occupational health and safety: Industrial case study. Journal of Engineering Research. 2023;11(3):238-248. DOI: 10.36909/jer.14153.
Altuntaş S, Dereli T, Erdoğan Z. Decision making in the manufacturing environment using the technique of precise order preference. Sigma Journal of Engineering and Natural Sciences. 2023;41(1):178-193.
Lundström A. Differentiated driving range: Exploring a solution to the problems with the" guess-o-meter" in electric cars. 6th international conference on automotive user interfaces and interactive vehicular applications, 17-19 Sept. 2014, Seattle, WiA USA. 2014. p. 1-8. DOI: 10.1145/2667317.2667347.
Kubiczek J, HadasikB. Segmentation of passenger electric cars market in Poland. World Electric Vehicle Journal. 2021; 12(1): 1-23. DOI: 10.3390/wevj12010023.
He H, et al. China's battery electric vehicles lead the world: achievements in technology system architecture and technological breakthroughs. Green Energy and Intelligent Transportation. 2022;1(1):1-24. DOI: 10.1016/j.geits.2022.100020.
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