APPLICATION OF VIKOR METHOD BASED ON CIRCULAR INTUITIONISTIC FUZZY SETS IN SELECTING AN ELECTRIFIED TRUCK

Abstract

The increasing population and evolving global consumer behaviors have fostered extensive commercial interactions worldwide. However, this expansion has resulted in heightened environmental pollution. Consequently, nations have initiated measures to mitigate air pollution, particularly stemming from transportation and shipping activities. As part of this endeavor, electric vehicles have emerged as a viable solution, steadily replacing fossil fuel-consuming vehicles and experiencing rapid proliferation. This study delves into the selection quandary surrounding electric trucks employed for transportation purposes. It delineates the criteria that decision-makers ought to consider during the selection process and evaluates market-available alternatives against these criteria. To contend with uncertainty and subjectivity inherent in the decision-making process, circular intuitive fuzzy numbers were employed. The VIKOR method, predicated on these numbers, was the preferred approach for assessing the alternatives. This research addresses a critical need in the realm of sustainable transportation, providing decision-makers with a systematic framework for evaluating electric trucks based on predefined criteria. By leveraging circular intuitive fuzzy numbers and the VIKOR method, the study offers a robust methodology for navigating the complexities inherent in selecting electric trucks, thereby advancing efforts to curtail air pollution and promote environmentally sustainable transportation practices.

Keywords

• VIKOR
• circular intuitionistic fuzzy set
• decision making
• electric truck
• environmental pollution

ELEKTRİKLİ KAMYON SEÇİMİNDE DAİRESEL SEZGİSEL BULANIK KÜMELERE DAYALI VIKOR YÖNTEMİNİN UYGULANMASI

Abstract

Artan nüfus ve gelişen küresel tüketici davranışları, dünya çapında kapsamlı ticari etkileşimleri teşvik etti. Ancak bu genişleme çevre kirliliğinin artmasına neden oldu. Sonuç olarak ülkeler, özellikle ulaşım ve nakliye faaliyetlerinden kaynaklanan hava kirliliğini azaltmak için önlemler almaya başladı. Bu çabanın bir parçası olarak, fosil yakıt tüketen araçların yerini alan ve hızla yaygınlaşan elektrikli araçlar, uygulanabilir bir çözüm olarak ortaya çıktı. Bu çalışma, ulaşım amacıyla kullanılan elektrikli kamyonların seçim konusundaki ikilemlerini incelemektedir. Karar vericilerin seçim sürecinde dikkate alması gereken kriterleri tanımlar ve piyasada mevcut alternatifleri bu kriterlere göre değerlendirir. Karar verme sürecinin doğasında olan belirsizlik ve öznellikle mücadele etmek için döngüsel sezgisel bulanık sayılar kullanıldı. Alternatiflerin değerlendirilmesinde bu sayılara dayalı VIKOR yöntemi tercih edilen yaklaşım olmuştur. Bu araştırma, sürdürülebilir ulaşım alanındaki kritik bir ihtiyacı ele alıyor ve karar vericilere elektrikli kamyonların önceden tanımlanmış kriterlere göre değerlendirilmesi için sistematik bir çerçeve sağlıyor. Döngüsel sezgisel bulanık sayılardan ve VIKOR yönteminden yararlanan bu çalışma, elektrikli kamyon seçiminin doğasında bulunan karmaşıklıkların üstesinden gelmek için sağlam bir metodoloji sunuyor ve böylece hava kirliliğini azaltma ve çevresel açıdan sürdürülebilir ulaşım uygulamalarını teşvik etme çabalarını ilerletiyor.

Keywords

• VIKOR
• dairesel sezgisel bulanık küme
• karar verme
• elektrikli kamyon
• çevre kirliliği

References

1. Ada, E., İlter, H.K., Sağnak, M., & Kazancıoğlu, Y. (2023). Smart technologies for collection and classification of electronic waste. International Journal of Quality and Reliability Management. https://doi.org/10.1108/IJQRM-08-2022-0259.
2. Aiello, G., Quaranta, S., Inguanta, R., Certa, A., & Venticinque, M. (2024). A multi-criteria decision-making framework for zero emission vehicle fleet renewal considering lifecycle and scenario uncertainty. Energies, 17, 1371. https://doi.org/10.3390/en17061371.
3. Albayrak, S., & Turanlı, M. (2022). Çok kriterli karar verme yöntemleri ile Türkiye’de HES (hidroelektrik santral) seçimi. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 21(41), 68-79. https://doi.org/10.55071/ticaretfbd.1113158.
4. Al Hazza, M., Dapit, A., Bourini, I.F., Muataz, Z., & Ali, M.Y. (2023). Multicriteria decision making on supplier selection using SOCCER model integrated with analytical hierarchy process. IIUM Engineering Journal, 24(2), 239-257. https://doi.org/10.31436/iiumej.v24i2.2787.
5. Arslan, R. (2018). AHP ile ağırlıklandırılmış VIKOR yöntemiyle araç seçimi: Rent a car firması uygulaması. Türk Akademik Sosyal Bilimler Araştırma Dergisi, 1(1), 15-20.
6. Atanassov, K.T. (2020). Circular intuitionistic fuzzy sets. Journal of Ambient Intelligent and Smart Environments, 39, 5981-5986.
7. Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87-96. https://doi.org/10.1016/S0165-011480034-3.
8. Bhavsar, D., Jaychandra, P.K., & Mittal, M. (2024). Data acquisition and performance analysis during real-time driving of a two-wheeler electric vehicle-A case study. World Electric Vehicle Journal, 15(3), https://doi.org/10.3390/wevj15030121.

9. Bošković, S., Švadlenka, L., Jovčić, S., Dobrodolac, M., Simić, V., & Bacanin, N. (2023). An alternative ranking order method accounting for two-step normalization (AROMAN)—A case study of the electric vehicle selection problem. IEEE Access, 11, 39496-39507.
10. Bozanic, D., Tešić, D., & Milićević, J. (2018). A hybrid fuzzy AHP-MABAC model: Application in the Serbian Army–The selection of the location for deep wading as a technique of crossing the river by tanks. Decision Making: Applications in Management and Engineering, 1(1), 143-164.
11. Chen, C.T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9.
12. Chen, T.Y. (2023). A circular intuitionistic fuzzy evaluation method based on distances from the average solution to support multiple criteria intelligent decisions involving uncertainty. Engineering Applications and Artificial Intelligence, 117, 105499. https://doi.org/10.1016/j.engappai.2022.105499.
13. Cogen, J. (2010). Report of the alternative fuel vehicle infrastructure of working group. USA, Oregon State Report.
14. Cuong, B.C. (2014). Picture fuzzy sets. Journal of Computer and Science Cybernetics, 30, 409-420.
15. Çaloğlu Büyükselçuk, E. & Tozan, H. (2022). Elektrikli araçların performanslarının CRITIC-EATWIOS ile değerlendirilmesi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 10, 1670-1688. https://doi.org/10.29130/dubited.1002851.
16. Das, M. C., Pandey, A., Mahato, A. K., & Singh, R. K. (2019). Comparative performance of electric vehicles using evaluation of mixed data. Opsearch, 56, 1067-1090.
17. Deng, J.W., Zhang, J.H., & Yang, S.X. (2024). Optimizing electric vehicle routing with nonlinear charging and time windows using improved differential evolution algorithm. Cluster Computing-The Journal of Network Software Tools and Applications, https://doi.org/10.1007/s10586-023-04243-z.
18. Efendi, A., & Fahmi, A.R. (2021). Design and build of electric car frame SULA evolution. Journal of Mechanical Engineering Education, 6(1), 11-21.
19. Erdoğan, S. (2020) Enerji, çevre ve sera gazları. Çankırı Karatekin Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 10(1), 277-303. https://doi.org/10.18074/ckuiibfd.670673.
20. Farinloye, T., Oluwatobi, O., Ugboma, O., Dickson, O.F. Uzondu, C., & Mogaji, E. (2024). Driving the electric vehicle agenda in Nigeria: The challenges, prospects and opportunities. Transportation Report Part D-Transport and Environment, 130, 104182. https://doi.org/10.1016/j.trd.2024.104182.
21. Fu, Y.K., Wu, C.J., & Liao, C.N. (2021). Selection of in-flight duty-free product suppliers using a combination fuzzy AHP, fuzzy ARAS, and MSGP methods. Mathematical Problems in Engineering, 2021, 545379.
22. Garibaldi, J.M., & Ozen, T. (2007). Uncertain fuzzy reasoning: A case study in modelling expert decision making. IEEE Transactions on Fuzzy Systems, 15, 16-30.
23. Grattan-Guiness, I. (1975). Fuzzy membership mapped onto interval and many-valued quantities. Mathematical Logic Quartely, 22, 149-160.
24. Güleç, M.A., & Ayvaz, B. (2021). İtfaiye istasyonlarındaki tehlikelerin çok kriterli karar verme yöntemleri ile ölçülmesi. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 20(39), 127-145.
25. Güven, F., & Rende, H. (2017). Elektrikli araçların tasarımında malzeme seçiminin önemi. Mühendis ve Makine, 58(689), 81-95.
26. Hassan, M.S., Ali, Y., Petrillo, A., & De Felice, F. (2023). Risk assessment of circular economy practices in construction industry of Pakistan. Science and Total Environment, 868, 161468. https://doi.org/10.1016/j.scitotenv.2023.161418.
27. Huang, Z., Yang, C., Zhou, X., and Gui, W. (2023). An improved TOPSIS-based multi-criteria decision-making approach for evaluating the working conditions of the aluminium cell. Engineering Applications and Artificial Intelligence, 117(105599). https://doi.org/10.1016/j.engappai.2022.105599.
28. Jahn, K.U. (1975). Intervall-wertige Mengen. Mathematische Nachrichten, 68, 115-132.
29. Jaller, M., & Otay, I. (2021). Evaluating Sustainable Vehicle Technologies for Freight Transportation Using Spherical Fuzzy AHP and TOPSIS. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., & Tolga, A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_15.
30. Kahraman, C., & Alkan, N. (2021). Circular intuitionistic fuzzy TOPSIS method with vague membership functions: Supplier selection application context. Notes on Intuitionistic Fuzzy Sets, 27, 24-52. doi: 10.7546/nifs.2021.27.1.24-52.
31. Kaur, H., Gupta, S., & Dhingra, A. (2023). Selection of solar panel using Entropy-TOPSIS technique. Materials Today:Proceedings, in Press, https://doi.org/10.1016/j.matpr.2023.02.034.
32. Kerem, A. (2014). Elektrikli araç teknolojisinin gelişimi ve gelecek beklentileri. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 5(1), 1-13.
33. Khan, M.J., Alcantud, J.C.R., Kumam, W., Kumam, P., & Alreshidi, N.A. (2023). Expanding pythagorean fuzzy sets with distinctive radii: Disc pythagorean fuzzy sets. Complex Intelligent Systems. https://doi.org/10.1007/s40747-023-01062-y.
34. Khan, M.J., Kumam, W., & Alreshidi, N.A. (2022). Divergence measures for circular intuitionistic fuzzy sets and their applications. Engineering Applications and Artificial Intelligence, 116, 105455. https://doi.org/10.1016/j.engappai.2022.105455.
35. Kijewska, K., Iwan, S., & Malecki, K. (2019). Applying multi-criteria analysis of electrically powered vehicles implementation in urban freight transport. Procedia Computer Science, 159, 1558-1567. https://doi.org/10.1016/j.procs.2019.
36. Kim, G. (2024). Electric vehicle routing problem with states of charging stations. Sustainability, 16(8). https://doi.org/10.3390/su16083439.
37. Krishnaprakash, S., Mariappan, R., & Broumi, S. (2024). Cubic spherical neutrosophic sets and selection of electric truck using cosine similarity measure. Neutrosophic Sets and Systems, 67(1).
38. Kutlu Gündoğdu, F., & Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent Fuzzy Systems, 36, 337-352.
39. Manik, M.H. (2023). Addressing the supplier selection problem by using the analytical hierarchy process. Heliyon, 9(7). https://doi.org/10.1016/j.heliyon.2023.e17997.
40. Meniz, B., Özkan, E.M. (2023). Vaccine selection for COVID-19 by AHP and novel VIKOR hybrid approach with interval type-2 fuzzy sets. Engineering Applications and Artificial Intelligence, 119, 105812. https://doi.org/10.1016/j.engappai.2022.105812.
41. Narang, M., Joshi, M.C., & Pal, A.K. (2021). A hybrid fuzzy COPRAS-base-criterion method for multi-criteria decision making. Soft Computing, 25(13), 8391-8399.
42. Oprocovic, S. (2011). Fuzzy VIKOR with an application to water resources planning. Expert Systems and Applications, 38, 12983-12990. https://doi.org/10.1016/j.eswa.2011.04.097.
43. Ourya, I., & Abderafi, S. (2023). Clean technology selection of hydrogen production on an industrial scale in Morocco. Results in Engineering, 17, 100815. https://doi.org/10.1016/j.rineng.2022.100815.
44. Panchal, D., Chatterjee, P., Shukla, R.K., Choudhury, T., & Tamosaitiene, J. (2017). Integrated Fuzzy AHP-Codas Framework for Maintenance Decision in Urea Fertilizer Industry. Economic Computation & Economic Cybernetics Studies & Research, 51(3).
45. Pasha, J., Li, B.K., Elmi, Z., Fathollahi-Fard, A.M., Lau, Y.Y. Roshani, A. Kawasaki, T., & Dulebenets, M.A. (2024). Electric vehicle scheduling: State of the art critical challenges, and future recent opportunities. Journal of Industrial Information Integration, 38, https://doi.org/10.1016/j.jii.2024.100561.
46. Pathak, D. K., Shankar, R., & Choudhary, A. (2021). Performance assessment framework based on competitive priorities for sustainable freight transportation systems. Transportation Research Part D: Transport and Environment, 90, 102663. https://doi.org/10.1016/j.trd.2020.102663.
47. Pennington, A.F., Cornwell, C.R., Sircar, K.D., & Mirabelli, M.C. (2024). Electric vehicles and health: A scoping review. Environmental Research, 251, 118697. https://doi.org/10.1016/j.envres.2024.118697.
48. Pouresmaeil, H., Khorram, E., & Shivanian, E. (2022). A parametric scoring function and the associated method for interval neutrosophic multi-criteria decision-making. Evolving Systems, 13(2), 347-359. https://doi.org/10.1007/s12530-021-09394-1.
49. Rani, P., Mishra, A.R., Mardani, A., Cavallaro, F., Streimikiene, D., & Khan, S.A.R. (2020). Pythagorean fuzzy SWARA-VIKOR framework for performance evaluation of solar panel selection. Sustainability, 12(10), 4278. https://doi.org/10.3390/su12104278.
50. Rong, P., & Pedram, M. (2003). An analytical model for predicting the remaining battery capacity of lithium-ion batteries. Proceedings of Design, Automation, and Test in Europe Conference and Exhibition, Munich, Germany, 1148-1149.
51. Sambuc, R. (1975). Fonctions φ-Floues. Application l’aide au Diagnostic en Pathologie Thyroidi- Enne. Ph. D. Thesis, University of Marseille, Marseille, France.
52. Sanguesa, J.A., Torres-Sanz, V., Garrido, P., Martinez, F.J., & Marquez-Barja, J. (2021). A review on electric vehicles: Technologies and challenges. Smart Cities, 4(1), 372-404.
53. Sejwal, R., Pal, S., Singh, N.K., Saini, R., & Yuvaraj, N. (2022). Selection of electric vehicles using MCDM techniques. Advanced Production and Industrial Engineering, IoS Press E-Book. https://doi.org/10.3233/ATDE220801.
54. Senapati, T., & Yager, R.R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11, 663-674.
55. Shammut, M., Cao, M., Zhang, Y., Papaix, C., Liu, Y., & Gao, X. (2019). Banning diesel vehicles in London: Is 2040 too late? Energies, 12(3495), 1-17.
56. Singh, V., Kumar, V., & Singh, V.B. (2023). A hybrid novel fuzzy AHP-TOPSIS technique for selecting parameter- influencing testing in software development. Decision Analysis Journal, 6(100159). https://doi.org/10.1016/j.dajour.2022.100159.
57. Smarandache, F. (1999). A Unifying Field in Logics. Neutrosophy: Neutrosophic Probability, Set and Logic, American Research Press: Rehoboth, DE, USA.
58. Sonar, H.C., & Kulkarni, S.D. (2021). An integrated AHP-MABAC approach for electric vehicle selection. Research in Transportation Business & Management, 41, 100665. https://doi.org/10.1016/j.rtbm.2021.100665.
59. Tian, G., Lu, W., Zhang, X., Zhan, M., Dulebenets, M. A:, Aleksandrov, A., Fathollahi-Fard, A. M., & Ivanov, M. (2023). A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems. Environmental Science and Pollution Resources, 30, 57279-57301. https://doi.org/10.1007/s11356-023-26577-2.
60. Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25, 529-539.
61. Türkiye İstatistik Kurumu, (2023). Sera Gazı Emisyon İstatistikleri 1990-2021, https://data.tuik.gov.tr/Bulten/Index?p=Sera-Gazi-Emisyon-Istatistikleri-1990-2021-49672#:~:text=Sera%20gaz%C4%B1%20envanteri%20sonu%C3%A7lar%C4%B1na%20g%C3%B6re,CO2%20e%C5%9Fd.%20olarak%20hesapland%C4%B1 adresinden 4 Mayıs 2024 tarihinde alınmıştır.
62. Ulutaş, A. (2019). Supplier selection by using a fuzzy integrated model for a textile company. Engineering Economics, 30(5), 579-590.
63. United Nations (2024). Transforming our World: The 2030 Agenda for Sustainable Development, https://sdgs.un.org/2030agenda adresinden 1 Mayıs 2024 tarihinde alınmıştır.
64. Valavanidis, A. (2018). The shift to diesel fuel engines and how the emission scandal of diesel vehicles unfolded. World Energy Consumption of Transportation Sector, 1, 1-26.
65. Voelcker. J. (2021). EVs explained: Battery capacity, gross versus net. https://www.caranddriver.com/features/a36051980/evs-explained-battery-capacity-grossversus-net/ adresinden 7 Mayıs 2024 tarihinde alınmıştır.
66. Wang, L., Ding, Y.F., Chen, Z.Y., Su, Z.Y., Zhuang, Y.F. (2024). Heuristic algorithms for heterogeneous and multi-trip electric vehicle routing problem with pickup and delivery. World Electric Vehicle Journal, 15(2). doi: 10.3390/wevj15020069.
67. Wang, N., Xu, Y., Puška, A., Stević, Ž., & Alrasheedi, A.F. (2023). Multi-criteria selection of electric delivery vehicles using fuzzy–rough methods. Sustainability, 15(21), 15541. https://doi.org/10.3390/su152115541.
68. Wappelhorst, S. (2024) The end of the road? An overview of combustion-engine car phase-out announcements across Europe, https://theicct.org/sites/default/files/publications/Combustion-engine-phase-out-briefingmay11.2020.pdf adresinden 2 Mayıs 2024 tarihinde alınmıştır.
69. Waseem, M., Amir, M., Lakshmi, G.S., Harivardhagini, S., & Ahmad, M. (2023). Fuel cell-based hybrid electric vehicles: An integrated review of current status, key challenges, recommended policies, and future prospects. Green Energy and Intelligent Transportation, 2, 100121. https://doi.org/10.1016/j.geits.2023.100121.
70. Wątróbski, J., Małecki, K., Kijewska, K., Iwan, S., Karczmarczyk, A., & Thompson, R.G. (2017). Multi-Criteria Analysis of Electric Vans for City Logistics. Sustainability, 9, 1453. https://doi.org/10.3390/su9081453.
71. Web of Science, (2024a). https://www.webofscience.com/wos/woscc/analyze-results/d8d06ffc-b5a9-44a5-82f5-15480441da9a-e9a2d78a adresinden 1 Mayıs 2024 tarihinde alınmıştır.
72. Web of Science, (2024b). https://www.webofscience.com/wos/woscc/analyze-results/a4505764-df3d-46ed-a8a9-a3db304c1cc0-e9b1cc28 adresinden 2 Mayıs 2024 tarihinde alınmıştır.
73. World Health Organization, (2024). https://www.who.int/teams/environment-climate-change-and-health/air-quality-energy-and-health/health-impacts/climate-impacts-of-air-pollution adresinden 30 Temmuz 2024 tarihinde alınmıştır.
74. Yager, R.R. (1986). On the theory of bags. International Journal of General System, 13, 23-37.
75. Yager, R.R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25, 1222-1230.
76. Yager, R.R. (2013). Pythagorean fuzzy subsets. In Proceedings of the Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), Edmonton, AB, Canada, 24–28 June 2013, 57–61.
77. Zadeh, L.A. (1965). Fuzzy sets. Information Control, 8, 338-353. doi:10.1016/S0019-995890241-X.
78. Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Science, 8, 199-249. doi: 10.1016/0020-025590036-5.
79. Zimmermann, H.J. (2001). Fuzzy Set Theory and Its Applications; Kluwer Academic Publishers: Alphen aan den Rijn, The Netherlands.
80. Zindani, D., Maity, S.R., & Bhowmik, S. (2019). Fuzzy-EDAS (evaluation based on distance from average solution) for material selection problems. In Advances in Computational Methods in Manufacturing, 755-771, Springer, Singapore.