Research Article
BibTex RIS Cite

Year 2022, Volume: 5 Issue: ICOLES2021 Special Issue, 38 - 47, 30.11.2022

Elif Çaloğlu Büyükselçuk Hakan Tozan

https://doi.org/10.34088/kojose.1015129
Cited By: 1

Abstract

References

  • [1] Choudhary, M. P., Garg, V., 2013. Causes, Consequences and Control of Air Pollution, Conference: All India Seminar on Methodologies for Air Pollution Control, India.
  • [2] Lozhkin, V., Lozhkina, O., Dobromirov, V., 2018. A Study of Air Pollution by Exhaust Gases from Cars in Well Courtyards of Saint Petersburg. Transportation Research Procedia, 36, pp. 453 – 458.
  • [3] Efendi, A., Fahmi, A. R., 2021. Design and Build of Electric Car Frame SULA Evolution. Journal of Mechanical Engineering Education, 6(1), pp. 11 – 21.
  • [4] 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), pp. 1 – 17.
  • [5] Xue, X. D., Cheng, K. W. E., Cheng, N. C., 2008. Selection of Electric Motor Drives for Electric Vehicles, Australasian Universities Power Engineering Conference, Australia, pp. 1 – 6.
  • [6] Baghdadi, M. E., Vroey, L. D., Coosemans, T., Mierlo, J. V., Foubert, W., Jahn, R., 2013. Electric Vehicle Performance and Consumption Evaluation. World Electric Vehicle Journal, 6, pp. 30 – 37.
  • [7] Laurikko, J., Granström, R., Haakana, A., 2012. Assessing Range and Performance of Electric Vehicles in Nordic Driving Conditions. World Electric Vehicle Journal, 5, 45 – 50.
  • [8] Zou, Z., Yun, Y., Sun, J., 2006. Entropy Method for Determination of Weight of Evaluating Indicators in Fuzzy Synthetic Evaluation for Water Quality Assessment. Journal of Environmental Sciences, 18(5), pp. 1020 – 1023.
  • [9] Özdağoğlu, A., Yakut, E., Bahar, S., 2017. Machine Selection in a Diary Product Company with Entropy and SAW Methods Integration. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 32(1), pp. 341 – 359.
  • [10] Chen, C. H., 2020. A Novel Multi-Criteria Decision-Making Model for Building Material Supplier Selection Based on Entropy-AHP Weighted TOPSIS. Entropy, 22(2), pp. 1 – 23.
  • [11] Lahsini, L., 2017. Maut Yöntemi Kombinasyonunda Entropi Yöntemine Göre Ağırlıklandırma. Akademik Sosyal Araştırmalar Dergisi, 5(41), pp. 501 – 512.
  • [12] Ömürbek, N., Karaatlı, M., Balcı, H. F., 2016. Entropi Temelli MAUT ve SAW Yöntemleri ile Otomotiv Firmalarının Performans Değerlemesi. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 31(1), pp. 227 – 255.
  • [13] Nyimbili, P. H., Erden, T., 2020. A Hybrid Approach Integrating Entropy-AHP and GIS for Suitability Assessment of Urban Emergency Facilities. International Journal of Geo-Information, 9(419), pp. 1 – 29.
  • [14] Kenger, M. D., 2017. Banka Personel Seçimin Çok Kriterli Karar Verme Yöntemlerinden Entropi Temelli MAUT, ARAS ve Gri İlişkisel Analiz Yöntemleri ile Değerlendirilmesi. Yüksek Lisans Tezi, Pamukkale Üniversitesi, İşletme Anabilim Dalı, Denizli.
  • [15] Özbek, A., Engür, M., 2018. EDAS Yöntemi ile Lojistik Firma Web Sitelerinin Değerlendirilmesi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 21(2), pp. 417 – 429.
  • [16] Yazdani, M., Torkayesh, A. E., Santibanez-Gonzalez, E. Dr., Otaghsara, S. K., 2020. Evaluation of Renewable Energy Resources Using Integrated Shannon Entropy-EDAS Model. Sustainable Operations and Computers, 1, pp. 35 – 42.
  • [17] Yalçın, N., Uncu, N., 2019. Applying EDAS as an Applicable MCDM Method for Industrial Robot Selection. Sigma Journal of Engineering and Natural Sciences, 37(3), pp. 779 – 796.
  • [18] Mitra, A., 2020. Selection of Cotton Fabrics Using EDAS Method. Journal of Natural Fibers, pp. 1-13.
  • [19] He, Y., Lei, F., Wei, G., Wang, R., Wu, J., Wei, C., 2019. EDAS Method for Multiple Attribute Group Decision Making with Probabilistic Uncertain Linguistic Information and Its Application to Green Supplier Selection. International Journal of Computational Intelligence Systems, 12(2), pp. 1361 – 1370.
  • [20] Stanujkic, D., Zavadskas, E. K., Ghorabaee, M. K., Turskis, Z., 2017. An Extension of The EDAS Method Based on The Use of Interval Grey Numbers. Studies in Informatics and Control, 26(1), pp. 5 – 12.
  • [21] Mathew, M., Sahu, S., 2018. Comparison of New Multi-Criteria Decision Making Methods for Material Handling Equipment Selection. Management Science Letters, 8(3), pp. 139 – 150.
  • [22] Chatterjee, P., Banerjee, A., Mondal, S., Boral, S., Chakraborty, S., 2018. Development of a Hybrid Meta-Model for Material Selection Using Design of Experiments and EDAS Method. Engineering Transactions, pp. 1 – 21.
  • [23] Caloglu Buyukselcuk, E., 2020. Cold Chain Logistics Firm Selection by Using AHP-VIKOR Integrated Method and a Case Study in Food Industry. pp. 403–415. https://doi.org/10.1007/978-3-030-31343-2_35.
  • [24] Hussain, S., Mandal, U., 2016. Entropy Based MCDM Approach for Selection of Material, National Level Conference on Engineering Problems and Application of Mathematics.
  • [25] Ersoy, N., 2018. Entropy Tabanlı Bütünleşik ÇKKV Yaklaşımı ile Kuramsal Sürdürülebilirlik Performans Ölçümü. Ege Akademik Bakış, 18(3), pp. 367 – 385.
  • [26] Ocampo, L., Deiparine, C.B., Go, A.L., 2020. Mapping Strategy to Best Practices for Sustainable Food Manufacturing Using Fuzzy DEMATEL-ANP-TOPSIS. Eng. Manag. J., 32, pp. 130–150. https://doi.org/10.1080/10429247.2020.1733379.
  • [27] Kaviani, M.A., Karbassi Yazdi, A., Ocampo, L., Kusi-Sarpong, S., 2019. An Integrated Grey-Based Multi-Criteria Decision-Making Approach for Supplier Evaluation and Selection in The Oil and Gas Industry. Kybernetes, 49, pp. 406–441. https://doi.org/10.1108/K-05-2018-0265.
  • [28] Yazdani, M., Chatterjee, P., Pamucar, D., Abad, M.D., 2019. A Risk-Based Integrated Decision-Making Model for Green Supplier Selection. Kybernetes, 49, pp. 1229–1252. https://doi.org/10.1108/K-09-2018-0509.
  • [29] Galankashi, M.R., Helmi, S.A., Hashemzahi, P., 2016. Supplier Selection in Automobile Industry: A Mixed Balanced Scorecard–Fuzzy AHP Approach. Alexandria Engineering Journal, 55, pp. 93–100. https://doi.org/10.1016/j.aej.2016.01.005.
  • [30] Liu, J.Y., Shiue, W., Chen, F.H., Huang, A.T., 2019. A Multiple Attribute Decision Making Approach in Evaluating Employee Care Strategies of Corporate Social Responsibility. Manag. Decis. 57, pp. 349–371. https://doi.org/10.1108/MD-03-2018-0230.
  • [31] Prakash, C., Barua, M.K., 2016. A Combined MCDM Approach for Evaluation and Selection of Third-Party Reverse Logistics Partner for Indian Electronics Industry. Sustain. Prod. Consum., 7, pp. 66–78. https://doi.org/10.1016/j.spc.2016.04.001.
  • [32] Mohammed, A., Harris, I., Dukyil, A., 2019. A Trasilient Decision Making Tool for Vendor Selection: A Hybrid-MCDM Algorithm. Manag. Decis., 57, pp. 372–395. https://doi.org/10.1108/MD-04-2018-0478.
  • [33] Dweiri, F., Kumar, S., Khan, S.A., Jain, V., 2016. Designing an Integrated AHP Based Decision Support System for Supplier Selection in Automotive Industry. Expert Syst. Appl., 62, pp. 273–283. https://doi.org/10.1016/j.eswa.2016.06.030.
  • [34] Merdivenci, F., Oğuz, S., 2020. Entropi Tabanlı EDAS Yöntemi ile Personel Seçimi: Lojistik Sektöründe Bir Uygulama. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 11(3), pp. 615 – 624.
  • [35] Özaydın, G., Karakul, A., 2021. Entropi Tabanlı MAUT, SAW ve EDAS Yöntemleri ile Finansal Performans Değerlendirmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(1), pp. 13 – 29.
  • [36] Ali, T., Ma, H., Nahian, A. J., 2019. An Analysis of The Renewable Energy Technology Selection in The Southern Region of Bangladesh Using a Hybrid Multi-Criteria Decision Making (MCDM) Method. International Journal of Renewable Energy Research, 9(4), pp. 1838 – 1848.
  • [37] Voelcker, J., 2021. EVs explained: Battery capacity, gross versus net. Retrieved from https://www.caranddriver.com/features/a36051980/evs-explained-battery-capacity-gross-versus-net/.
  • [38] Erhan, K., Ayaz, M., Özdemir, E., 2013. Elektrikli Araç Şarj İstasyonlarının Güç Kalitesi Üzerine Etkileri. Akıllı Şebekeler ve Türkiye Elektrik Şebekesinin Geleceği Sempozyumu, Ankara.
  • [39] Gasbaoui, B., Chaker, A., Laoufi, A., Allaoua, B., Nasri, A., 2011. The Efficiency of Direct Torque Control for Electric Vehicle Behaviour Improvement. Serbian Journal of Electrical Engineering, 8(2), pp. 127 – 146.
  • [40] 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), pp. 372 – 404.
  • [41] Grunditz, E. A., Thiringer, T., 2018. Electric Vehicle Acceleration Performance and Motor Drive Cycle Energy Efficiency Trade-Off. XIII. International Conference on Electrical Machines, pp. 717 – 723.
  • [42] Yüksekyıldız, E., 2021. Entropi ve EATWOS Yöntemleri ile Türkiye Konteyner Limanlarının Verimlilik Analizi. Verimlilik Dergisi, 2, pp. 3 – 24.
  • [43] Wu, J., Sun, J., Liang, L., Zha, Y., 2011. Determination of Weights for Ultimate Cross Efficiency Using Shannon Entropy. Expert Systems with Applications, 38(5), pp. 5162 – 5165.
  • [44] Yüksekyıldız, E., 2020. Türkiye Kruvaziyer Limanlarının Performans Değerlendirmesi. European Journal of Science and Technology, 3, pp. 607 – 615.
  • [45] Keshavarz Ghorabaee, M., Zavadskas, E.K., Olfat, L., Turskis, Z., 2015. Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance From Average Solution (EDAS). Informatica, 26, pp. 435–451.
  • [46] Ulutaş, A., 2017, EDAS Yöntemi Kullanılarak Bir Tekstil Atölyesi için Dikiş Makinesi Seçimi. İşletme Araştırmaları Dergisi, 9(2), pp. 169-183.
  • [47] Gabbatiss, J., 2019. Electric Vehicles Already Able to Cut Greenhouse Gas Emissions by Half. The Independent, ESI Media.
  • [48] Nealer, R., Reichmuth, D., Anair, D., 2015. Cleaner Cars from Cradle to Grave: How Electric Cars Beat Gasoline Cars on Lifetime Global Warming Emissions. Cambridge: Union of Concerned Scientists.
  • [49] Dunning, B., 2019. No, Electric Cars Don’t Pollute More. Retrieved from https://skeptoid.com/episodes/ 4687?fbclid=IwAR3TjNmLH9rIft2uJkLeS7ksXzyvR8XOklutpBJQLFSqGOu2AZvBbg0MUtA.

Integrated Entropy-EDAS Methods for the Electrified Car Selection Problem

Year 2022, Volume: 5 Issue: ICOLES2021 Special Issue, 38 - 47, 30.11.2022

Elif Çaloğlu Büyükselçuk Hakan Tozan

https://doi.org/10.34088/kojose.1015129
Cited By: 1

Abstract

Increasing air pollution affects the environment and life negatively. For a sustainable environment and life, people, voluntary organizations, and governments need to work on the solution of this problem. The biggest sources of air pollution are transportation vehicles. For this reason, many countries in Europe have stated that they will use solely electrified cars to reduce air pollution in the future. Therefore, in this study, it is aimed to determine the best electrified car. The result obtained can support consumers that to intend to buy an electrified vehicle in the decision-making process. This problem is a typical multi-criteria decision making (MCDM) problem and some MCDM techniques are used to solve these problems. Here, the Entropy method was used to determine the weights of the selection criteria. Selection criteria was determined according to comprehensive literature survey and interviews with sales representatives. The EDAS (Evaluation based on Distance from Average Solution) method was used to rank the electrified car alternatives that sold in Turkey. As a result of the evaluation, the most important criteria was determined by the price of the vehicle, the net battery capacity, and the electric motor power. According to these criteria, the electrified car manufactured in China was chosen as the best.

Keywords

Air Pollution Decision Making EDAS Electrified Car Entropy

References

  • [1] Choudhary, M. P., Garg, V., 2013. Causes, Consequences and Control of Air Pollution, Conference: All India Seminar on Methodologies for Air Pollution Control, India.
  • [2] Lozhkin, V., Lozhkina, O., Dobromirov, V., 2018. A Study of Air Pollution by Exhaust Gases from Cars in Well Courtyards of Saint Petersburg. Transportation Research Procedia, 36, pp. 453 – 458.
  • [3] Efendi, A., Fahmi, A. R., 2021. Design and Build of Electric Car Frame SULA Evolution. Journal of Mechanical Engineering Education, 6(1), pp. 11 – 21.
  • [4] 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), pp. 1 – 17.
  • [5] Xue, X. D., Cheng, K. W. E., Cheng, N. C., 2008. Selection of Electric Motor Drives for Electric Vehicles, Australasian Universities Power Engineering Conference, Australia, pp. 1 – 6.
  • [6] Baghdadi, M. E., Vroey, L. D., Coosemans, T., Mierlo, J. V., Foubert, W., Jahn, R., 2013. Electric Vehicle Performance and Consumption Evaluation. World Electric Vehicle Journal, 6, pp. 30 – 37.
  • [7] Laurikko, J., Granström, R., Haakana, A., 2012. Assessing Range and Performance of Electric Vehicles in Nordic Driving Conditions. World Electric Vehicle Journal, 5, 45 – 50.
  • [8] Zou, Z., Yun, Y., Sun, J., 2006. Entropy Method for Determination of Weight of Evaluating Indicators in Fuzzy Synthetic Evaluation for Water Quality Assessment. Journal of Environmental Sciences, 18(5), pp. 1020 – 1023.
  • [9] Özdağoğlu, A., Yakut, E., Bahar, S., 2017. Machine Selection in a Diary Product Company with Entropy and SAW Methods Integration. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 32(1), pp. 341 – 359.
  • [10] Chen, C. H., 2020. A Novel Multi-Criteria Decision-Making Model for Building Material Supplier Selection Based on Entropy-AHP Weighted TOPSIS. Entropy, 22(2), pp. 1 – 23.
  • [11] Lahsini, L., 2017. Maut Yöntemi Kombinasyonunda Entropi Yöntemine Göre Ağırlıklandırma. Akademik Sosyal Araştırmalar Dergisi, 5(41), pp. 501 – 512.
  • [12] Ömürbek, N., Karaatlı, M., Balcı, H. F., 2016. Entropi Temelli MAUT ve SAW Yöntemleri ile Otomotiv Firmalarının Performans Değerlemesi. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 31(1), pp. 227 – 255.
  • [13] Nyimbili, P. H., Erden, T., 2020. A Hybrid Approach Integrating Entropy-AHP and GIS for Suitability Assessment of Urban Emergency Facilities. International Journal of Geo-Information, 9(419), pp. 1 – 29.
  • [14] Kenger, M. D., 2017. Banka Personel Seçimin Çok Kriterli Karar Verme Yöntemlerinden Entropi Temelli MAUT, ARAS ve Gri İlişkisel Analiz Yöntemleri ile Değerlendirilmesi. Yüksek Lisans Tezi, Pamukkale Üniversitesi, İşletme Anabilim Dalı, Denizli.
  • [15] Özbek, A., Engür, M., 2018. EDAS Yöntemi ile Lojistik Firma Web Sitelerinin Değerlendirilmesi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 21(2), pp. 417 – 429.
  • [16] Yazdani, M., Torkayesh, A. E., Santibanez-Gonzalez, E. Dr., Otaghsara, S. K., 2020. Evaluation of Renewable Energy Resources Using Integrated Shannon Entropy-EDAS Model. Sustainable Operations and Computers, 1, pp. 35 – 42.
  • [17] Yalçın, N., Uncu, N., 2019. Applying EDAS as an Applicable MCDM Method for Industrial Robot Selection. Sigma Journal of Engineering and Natural Sciences, 37(3), pp. 779 – 796.
  • [18] Mitra, A., 2020. Selection of Cotton Fabrics Using EDAS Method. Journal of Natural Fibers, pp. 1-13.
  • [19] He, Y., Lei, F., Wei, G., Wang, R., Wu, J., Wei, C., 2019. EDAS Method for Multiple Attribute Group Decision Making with Probabilistic Uncertain Linguistic Information and Its Application to Green Supplier Selection. International Journal of Computational Intelligence Systems, 12(2), pp. 1361 – 1370.
  • [20] Stanujkic, D., Zavadskas, E. K., Ghorabaee, M. K., Turskis, Z., 2017. An Extension of The EDAS Method Based on The Use of Interval Grey Numbers. Studies in Informatics and Control, 26(1), pp. 5 – 12.
  • [21] Mathew, M., Sahu, S., 2018. Comparison of New Multi-Criteria Decision Making Methods for Material Handling Equipment Selection. Management Science Letters, 8(3), pp. 139 – 150.
  • [22] Chatterjee, P., Banerjee, A., Mondal, S., Boral, S., Chakraborty, S., 2018. Development of a Hybrid Meta-Model for Material Selection Using Design of Experiments and EDAS Method. Engineering Transactions, pp. 1 – 21.
  • [23] Caloglu Buyukselcuk, E., 2020. Cold Chain Logistics Firm Selection by Using AHP-VIKOR Integrated Method and a Case Study in Food Industry. pp. 403–415. https://doi.org/10.1007/978-3-030-31343-2_35.
  • [24] Hussain, S., Mandal, U., 2016. Entropy Based MCDM Approach for Selection of Material, National Level Conference on Engineering Problems and Application of Mathematics.
  • [25] Ersoy, N., 2018. Entropy Tabanlı Bütünleşik ÇKKV Yaklaşımı ile Kuramsal Sürdürülebilirlik Performans Ölçümü. Ege Akademik Bakış, 18(3), pp. 367 – 385.
  • [26] Ocampo, L., Deiparine, C.B., Go, A.L., 2020. Mapping Strategy to Best Practices for Sustainable Food Manufacturing Using Fuzzy DEMATEL-ANP-TOPSIS. Eng. Manag. J., 32, pp. 130–150. https://doi.org/10.1080/10429247.2020.1733379.
  • [27] Kaviani, M.A., Karbassi Yazdi, A., Ocampo, L., Kusi-Sarpong, S., 2019. An Integrated Grey-Based Multi-Criteria Decision-Making Approach for Supplier Evaluation and Selection in The Oil and Gas Industry. Kybernetes, 49, pp. 406–441. https://doi.org/10.1108/K-05-2018-0265.
  • [28] Yazdani, M., Chatterjee, P., Pamucar, D., Abad, M.D., 2019. A Risk-Based Integrated Decision-Making Model for Green Supplier Selection. Kybernetes, 49, pp. 1229–1252. https://doi.org/10.1108/K-09-2018-0509.
  • [29] Galankashi, M.R., Helmi, S.A., Hashemzahi, P., 2016. Supplier Selection in Automobile Industry: A Mixed Balanced Scorecard–Fuzzy AHP Approach. Alexandria Engineering Journal, 55, pp. 93–100. https://doi.org/10.1016/j.aej.2016.01.005.
  • [30] Liu, J.Y., Shiue, W., Chen, F.H., Huang, A.T., 2019. A Multiple Attribute Decision Making Approach in Evaluating Employee Care Strategies of Corporate Social Responsibility. Manag. Decis. 57, pp. 349–371. https://doi.org/10.1108/MD-03-2018-0230.
  • [31] Prakash, C., Barua, M.K., 2016. A Combined MCDM Approach for Evaluation and Selection of Third-Party Reverse Logistics Partner for Indian Electronics Industry. Sustain. Prod. Consum., 7, pp. 66–78. https://doi.org/10.1016/j.spc.2016.04.001.
  • [32] Mohammed, A., Harris, I., Dukyil, A., 2019. A Trasilient Decision Making Tool for Vendor Selection: A Hybrid-MCDM Algorithm. Manag. Decis., 57, pp. 372–395. https://doi.org/10.1108/MD-04-2018-0478.
  • [33] Dweiri, F., Kumar, S., Khan, S.A., Jain, V., 2016. Designing an Integrated AHP Based Decision Support System for Supplier Selection in Automotive Industry. Expert Syst. Appl., 62, pp. 273–283. https://doi.org/10.1016/j.eswa.2016.06.030.
  • [34] Merdivenci, F., Oğuz, S., 2020. Entropi Tabanlı EDAS Yöntemi ile Personel Seçimi: Lojistik Sektöründe Bir Uygulama. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 11(3), pp. 615 – 624.
  • [35] Özaydın, G., Karakul, A., 2021. Entropi Tabanlı MAUT, SAW ve EDAS Yöntemleri ile Finansal Performans Değerlendirmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(1), pp. 13 – 29.
  • [36] Ali, T., Ma, H., Nahian, A. J., 2019. An Analysis of The Renewable Energy Technology Selection in The Southern Region of Bangladesh Using a Hybrid Multi-Criteria Decision Making (MCDM) Method. International Journal of Renewable Energy Research, 9(4), pp. 1838 – 1848.
  • [37] Voelcker, J., 2021. EVs explained: Battery capacity, gross versus net. Retrieved from https://www.caranddriver.com/features/a36051980/evs-explained-battery-capacity-gross-versus-net/.
  • [38] Erhan, K., Ayaz, M., Özdemir, E., 2013. Elektrikli Araç Şarj İstasyonlarının Güç Kalitesi Üzerine Etkileri. Akıllı Şebekeler ve Türkiye Elektrik Şebekesinin Geleceği Sempozyumu, Ankara.
  • [39] Gasbaoui, B., Chaker, A., Laoufi, A., Allaoua, B., Nasri, A., 2011. The Efficiency of Direct Torque Control for Electric Vehicle Behaviour Improvement. Serbian Journal of Electrical Engineering, 8(2), pp. 127 – 146.
  • [40] 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), pp. 372 – 404.
  • [41] Grunditz, E. A., Thiringer, T., 2018. Electric Vehicle Acceleration Performance and Motor Drive Cycle Energy Efficiency Trade-Off. XIII. International Conference on Electrical Machines, pp. 717 – 723.
  • [42] Yüksekyıldız, E., 2021. Entropi ve EATWOS Yöntemleri ile Türkiye Konteyner Limanlarının Verimlilik Analizi. Verimlilik Dergisi, 2, pp. 3 – 24.
  • [43] Wu, J., Sun, J., Liang, L., Zha, Y., 2011. Determination of Weights for Ultimate Cross Efficiency Using Shannon Entropy. Expert Systems with Applications, 38(5), pp. 5162 – 5165.
  • [44] Yüksekyıldız, E., 2020. Türkiye Kruvaziyer Limanlarının Performans Değerlendirmesi. European Journal of Science and Technology, 3, pp. 607 – 615.
  • [45] Keshavarz Ghorabaee, M., Zavadskas, E.K., Olfat, L., Turskis, Z., 2015. Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance From Average Solution (EDAS). Informatica, 26, pp. 435–451.
  • [46] Ulutaş, A., 2017, EDAS Yöntemi Kullanılarak Bir Tekstil Atölyesi için Dikiş Makinesi Seçimi. İşletme Araştırmaları Dergisi, 9(2), pp. 169-183.
  • [47] Gabbatiss, J., 2019. Electric Vehicles Already Able to Cut Greenhouse Gas Emissions by Half. The Independent, ESI Media.
  • [48] Nealer, R., Reichmuth, D., Anair, D., 2015. Cleaner Cars from Cradle to Grave: How Electric Cars Beat Gasoline Cars on Lifetime Global Warming Emissions. Cambridge: Union of Concerned Scientists.
  • [49] Dunning, B., 2019. No, Electric Cars Don’t Pollute More. Retrieved from https://skeptoid.com/episodes/ 4687?fbclid=IwAR3TjNmLH9rIft2uJkLeS7ksXzyvR8XOklutpBJQLFSqGOu2AZvBbg0MUtA.
There are 49 citations in total.

Details

Primary Language English
Subjects Industrial Engineering
Journal Section Articles
Authors

Elif Çaloğlu Büyükselçuk 0000-0002-5976-6727

Hakan Tozan 0000-0002-0479-6937

Early Pub Date June 30, 2022
Publication Date November 30, 2022
Acceptance Date June 6, 2022
Published in Issue Year 2022 Volume: 5 Issue: ICOLES2021 Special Issue

Cite

APA Çaloğlu Büyükselçuk, E., & Tozan, H. (2022). Integrated Entropy-EDAS Methods for the Electrified Car Selection Problem. Kocaeli Journal of Science and Engineering, 5(ICOLES2021 Special Issue), 38-47. https://doi.org/10.34088/kojose.1015129

Cited By

Selection of a sustainable location for waste electrical and electronic equipment recycling plant using Entropy and Evaluation based on Distance from Average Solution
Waste Management & Research: The Journal for a Sustainable Circular Economy
https://doi.org/10.1177/0734242X231223903