Location Selection of Electric Vehicle Charging Stations Through Employing the Spherical Fuzzy CoCoSo and CRITIC Technique

Yan, Rong; Han, Yongguang; Zhang, Huiyuan; Wei, Cun

doi:10.15388/24-INFOR545

Informatica

Location Selection of Electric Vehicle Charging Stations Through Employing the Spherical Fuzzy CoCoSo and CRITIC Technique

Volume 35, Issue 1 (2024), pp. 203–225

Rong Yan Yongguang Han Huiyuan Zhang Cun Wei

https://doi.org/10.15388/24-INFOR545

Pub. online: 8 March 2024 Type: Research Article

Open Access

Received
1 October 2022

Accepted
1 February 2024

Published
8 March 2024

Abstract

Energy conservation and emission reduction are important policies vigorously promoted in China. With the continuous popularization of the concept of green transportation, electric vehicles have become a green transportation tool with good development prospects, greatly reducing the pressure on the environment and resources caused by rapid economic growth. The development status of electric vehicles has a significant impact on urban energy security, environmental protection, and sustainable development in China. With the widespread application of new energy vehicles, charging piles have become an important auxiliary infrastructure necessary for the development of electric vehicles. They have significant social and economic benefits, so it is imperative to build electric vehicle charging piles. There are many factors to consider in the scientific layout of electric vehicle charging stations, and the location selection problem of electric vehicle charging stations is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Combined Compromise Solution (CoCoSo) technique and CRITIC technique have been utilized to deal with MAGDM issues. Spherical fuzzy sets (SFSs) can uncover the uncertainty and fuzziness in MAGDM more effectively and deeply. In this paper, on basis of CoCoSo technique, a novel spherical fuzzy number CoCoSo (SFN-CoCoSo) technique based on spherical fuzzy number cosine similarity measure (SFNCSM) and spherical fuzzy number Euclidean distance (SFNED) is conducted for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the CRITIC technique is extended to SFSs to acquire the attribute weights based on the SFNCSM and SFNED. Finally, the SFN-CoCoSo technique is utilized for location selection problem of electric vehicle charging stations to prove practicability of the developed technique and compare the SFN-CoCoSo technique with existing techniques to further demonstrate its superiority.

References

Ahn, N., Jo, S.Y., Kang, S.J. (2019). Constraint-aware electricity consumption estimation for prevention of overload by electric vehicle charging station. Energies, 12(6), 1000.

Asna, M., Shareef, H., Muhammad, M.A., Ismail, L., Prasanthi, A. (2022). Multi-objective quantum atom search optimization algorithm for electric vehicle charging station planning. International Journal of Energy Research, 46(12), 17308–17331.

Aydin, S., Kahraman, C. (2020). A spherical fuzzy multi expert MCDM method based on the entropy and cosine similarity. In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific, pp. 157–164.

Aydogdu, A., Gul, S. (2020). A novel entropy proposition for spherical fuzzy sets and its application in multiple attribute decision-making. International Journal of Intelligent Systems, 35(9), 1354–1374.

Badi, I., Alosta, A., Elmansouri, O., Abdulshahed, A., Elsharief, S. (2023). An application of a novel grey-CODAS method to the selection of hub airport in North Africa. Decision Making: Applications in Management and Engineering, 6(1), 18–33.

Banegas, J., Mamkhezri, J. (2023). A systematic review of geographic information systems based methods and criteria used for electric vehicle charging station site selection. Environmental Science and Pollution Research, 30, 68054–68083.

Bao, Z.Y., Xie, C. (2021). Optimal station locations for en-route charging of electric vehicles in congested intercity networks: a new problem formulation and exact and approximate partitioning algorithms. Transportation Research Part C: Emerging Technologies, 133, 103447.

Bian, H.H., Zhou, C.G., Guo, Z.Y., Wang, X.M., He, Y., Peng, S. (2022). Planning of electric vehicle fast-charging station based on POI interest point division, functional area, and multiple temporal and spatial characteristics. Energy Reports, 8, 831–840.

Chen, X., Ding, Z., Dong, Y., Liang, H. (2021). Managing consensus with minimum adjustments in group decision making with opinions evolution. IEEE Transactions on Systems Man Cybernetics-Systems, 51(4), 2299–2311.

Cui, F.B., You, X.Y., Shi, H., Liu, H.C. (2018). Optimal siting of electric vehicle charging stations using Pythagorean fuzzy VIKOR approach. Mathematical Problems in Engineering, 2018, 9262067.

Cuong, B.C. (2014). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4), 409–420.

Diakoulaki, D., Mavrotas, G., Papayannakis, L. (1995). Determining objective weight in multple criteria problems: the CRITIC method. Computers & Operations Research, 22(7), 763–770.

Dong, Y., Li, Y., He, Y., Chen, X. (2021). Preference-approval structures in group decision making: axiomatic distance and aggregation. Decision Analysis, 18(4), 273–295.

Erbas, M., Kabak, M., Ozceylan, E., Cetinkaya, C. (2018). Optimal siting of electric vehicle charging stations: a GIS-based fuzzy multi-criteria decision analysis. Energy, 163, 1017–1031.

Fredriksson, H., Dahl, M., Holmgren, J. (2019). Optimal placement of charging stations for electric vehicles in large-scale transportation networks. Procedia Computer Science, 160, 77–84.

Garg, H. (2021). Multi-attribute group decision-making process based on possibility degree and operators for intuitionistic multiplicative set. Complex & Intelligent Systems, 7, 1099–1121.

Garg, H., Naz, S., Ziaa, F., Shoukat, Z. (2021a). A ranking method based on Muirhead mean operator for group decision making with complex interval-valued q-rung orthopair fuzzy numbers. Soft Computing, 25, 14001–14027.

Garg, H., Ullah, K., Mahmood, T., Hassan, N., Jan, N. (2021b). T-spherical fuzzy power aggregation operators and their applications in multi-attribute decision making. Journal of Ambient Intelligence and Humanized Computing, 12, 9067–9080.

Gundogdu, F.K., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems, 36(1), 337–352.

He, J., Yang, X.L., Deng, C.H., Liu, G.G., Huang, W.T., Zhu, L.W. (2022). Expansion planning of electric vehicle charging stations considering the benefits of peak-regulation frequency modulation. IET Generation, Transmission & Distribution, 16(7), 1400–1415.

Huang, M.F., Shen, H., Peng, J.J., Zhang, L. (2022). Research on fault recovery method of AC/DC hybrid distribution network with electric vehicle charging station. Energy Reports, 8, 273–279.

Jiang, Y., Wan, J.P. (2020). Research on location of electric vehicle charging station based on Voronoi diagram and improved particle swarm algorithm. In: 19th Annual Wuhan International Conference on E-Business (WHICEB), Wuhan, China, pp. 450–460.

Ju, Y.B., Ju, D.W., Gonzalez, E., Giannakis, M., Wang, A.H. (2019). Study of site selection of electric vehicle charging station based on extended GRP method under picture fuzzy environment. Computers & Industrial Engineering, 135, 1271–1285.

Karasan, A., Kaya, I., Erdogan, M. (2020). Location selection of electric vehicles charging stations by using a fuzzy MCDM method: a case study in Turkey. Neural Computing and Applications, 32, 4553–4574.

Kharwar, P.K., Verma, R.K., Singh, A. (2022). Neural network modeling and combined compromise solution (CoCoSo) method for optimization of drilling performances in polymer nanocomposites. Journal of Thermoplastic Composite Materials, 35(10), 1604–1631.

Kizhakkan, A.R., Rathore, A.K., Awasthi, A. (2019). Review of electric vehicle charging station location planning. In: 2019 IEEE Transportation Electrification Conference (ITEC-India), Bengaluru, India, pp. 1–5. https://doi.org/10.1109/ITEC-India48457.2019.ITECINDIA2019-226.

Kong, C.Y., Jovanovic, R., Bayram, I.S., Devetsikiotis, M. (2017). A hierarchical optimization model for a network of electric vehicle charging stations. Energies, 10(5), 675. https://doi.org/10.3390/en10050675.

Kutlu Gündoğdu, F., Kahraman, C. (2021). Optimal site selection of electric vehicle charging station by using spherical fuzzy TOPSIS method. In: Kahraman, C., Kutlu Gündoğdu, F. (Eds.), Decision Making with Spherical Fuzzy Sets. Studies in Fuzziness and Soft Computing, Vol. 392. Springer, Cham, pp. 201–216. https://doi.org/10.1007/978-3-030-45461-6_8.

Lai, H., Liao, H.C., Long, Y.L., Zavadskas, E.K. (2022). A hesitant fermatean fuzzy CoCoSo method for group decision-making and an application to blockchain platform evaluation. International Journal of Fuzzy Systems, 24, 2643–2661.

Li, S.W., Fan, X.L., Gao, S., Sun, Q., Bai, X.Z. (2017). Research on two-stage planning method of electric vehicle charging station based on immune algorithm. In: 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, 2017, pp. 1–6. https://doi.org/10.1109/EI2.2017.8245441.

Li, Y.J., Pei, W.H., Xu, D. (2021). Electric vehicle charging station planning based on immune algorithm and Voronoi diagram. In: 2021 40th Chinese Control Conference (CCC), Shanghai, China, 2021, pp. 1532–1537. https://doi.org/10.23919/CCC52363.2021.9549436.

Li, J.L., Liu, Z.B., Wang, X.F. (2022a). Public charging station localization and route planning of electric vehicles considering the operational strategy: a bi-level optimizing approach. Sustainable Cities and Society, 87, 104153.

Li, Y.J., Pei, W.H., Zhang, Q. (2022b). Improved whale optimization algorithm based on hybrid strategy and its application in location selection for electric vehicle charging stations. Energies, 15(19), 7035.

Liang, D.C., Li, F.S., Wang, M.W., Xu, Z.S. (2022). Two-stage assignment classification model based on an improved AHPSort II with heterogeneous criteria for location selection of electric vehicle charging stations. IEEE Transactions on Engineering Management, 71, 2241–2254.

Liao, H.C., Kuang, L.S., Liu, Y.X., Tang, M. (2021). Non-cooperative behavior management in group decision making by a conflict resolution process and its implementation for pharmaceutical supplier selection. Information Sciences, 567, 131–145.

Lin, W.T., Hua, G.W. (2015). The flow capturing location model and algorithm of electric vehicle charging stations. In: 2015 International Conference on Logistics, Informatics and Service Sciences (LISS), Barcelona, Spain, 2015, pp. 1–6. https://doi.org/10.1109/LISS.2015.7369788.

Lin, Y., Wang, Y.M., Chen, S.Q. (2017). Hesitant fuzzy multiattribute matching decision making based on regret theory with uncertain weights. International Journal of Fuzzy Systems, 19, 955–966.

Liu, A.J., Zhao, Y.X., Meng, X.G., Zhang, Y. (2020). A three-phase fuzzy multi-criteria decision model for charging station location of the sharing electric vehicle. International Journal of Production Economics, 225, 107572.

Liu, C.C., Xiang, X., Zhang, C.R., Wang, Q., Zheng, L. (2018). A column generation based distributed scheduling algorithm for multi-mode resource constrained project scheduling problem. Computers & Industrial Engineering, 125, 258–278.

Liu, H.C., Yang, M.Y., Zhou, M.C., Tian, G.D. (2019). An integrated multi-criteria decision making approach to location planning of electric vehicle charging stations. Ieee Transactions on Intelligent Transportation Systems, 20(1), 362–373.

Liu, P.D., Zhu, B.Y., Wang, P. (2019). A multi-attribute decision-making approach based on spherical fuzzy sets for Yunnan Baiyao’s R&D project selection problem. International Journal of Fuzzy Systems, 21(1), 2168–2191.

Luo, Q.Y., Tian, W.L., Jia, H.F. (2020). Location and capacity model of charging station for electric vehicles based on commuting demand. IEEJ Transactions on Electrical and Electronic Engineering, 15(7), 1089–1099.

Mahmood, T., Ullah, K., Khan, Q., Jan, N. (2019). An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing & Applications, 31, 7041–7053.

Narang, M., Joshi, M.C., Bisht, K., Pal, A. (2022). Stock portfolio selection using a new decision-making approach based on the integration of fuzzy CoCoSo with Heronian mean operator. Decision Making: Applications in Management and Engineering, 5(1), 90–112.

Pamucar, D., Žižović, M., Ðuričić, D. (2022). Modification of the CRITIC method using fuzzy rough numbers. Decision Making: Applications in Management and Engineering, 5(2), 362–371.

Palanikumar, M., Kausar, N., Garg, H., Kadry, S., Kim, J. (2023). Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation. Alexandria Engineering Journal, 77, 149–164.

Peng, X.D., Smarandache, F. (2020). A decision-making framework for China’s rare earth industry security evaluation by neutrosophic soft CoCoSo method. Journal of Intelligent & Fuzzy Systems, 39(5), 7571–7585.

Rani, P., Mishra, A.R. (2021). Fermatean fuzzy Einstein aggregation operators-based MULTIMOORA method for electric vehicle charging station selection. Expert Systems with Applications, 182, 115267.

Saghari, A., Budinská, I., Hosseinimehr, M., Rahmani, S. (2023). A robust-reliable decision-making methodology based on a combination of Stakeholders’ preferences simulation and KDD techniques for selecting automotive platform benchmark. Symmetry, 15(3), 750.

Sałabun, W., Urbaniak, K. (2020). A new coefficient of rankings similarity in decision-making problems. In: 20th Annual International Conference on Computational Science (ICCS), Amsterdam, Netherlands, 2020, Springer, Cham, pp. 632–645.

Sałabun, W., Wątróbski, J., Shekhovtsov, A. (2020). Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry, 12(9), 1549.

Sankar, S.M.U., Kumar, N.J., Elangovan, G., Praveen, R. (2023). An integrated Z-number and DEMATEL-based cooperation enforcement scheme for thwarting malicious nodes in MANETs. Wireless Personal Communications, 130, 2531–2563.

Seikh, M.R., Mandal, U. (2022). Multiple attribute group decision making based on quasirung orthopair fuzzy sets: application to electric vehicle charging station site selection problem. Engineering Applications of Artificial Intelligence, 115, 105299.

Shabu, S., Yadav, K., Kariri, E., Gola, K.K., AnulHaq, M., Kumar, A. (2023). Trajectory clustering and query processing analysis framework for knowledge discovery in cloud environment. Expert Systems, 40(4), e12968.

Sharaf, I.M. (2021). Spherical fuzzy VIKOR with SWAM and SWGM operators for MCDM. In: Kahraman, C., Kutlu Gündoğdu, F. (Eds.), Decision Making with Spherical Fuzzy Sets. Studies in Fuzziness and Soft Computing, Vol. 392. Springer, Cham, pp. 217–240. https://doi.org/10.1007/978-3-030-45461-6_9.

Sisman, A. (2023). Identification of suitable sites for electric vehicle charging stations; a geographical information systems based multi criteria decision making approach. Energy Sources Part A: Recovery, Utilization, and Environmental Effects, 45, 4017–4030.

Tian, X.L., Xu, Z.S., Gu, J., Herrera, F. (2021). A consensus process based on regret theory with probabilistic linguistic term sets and its application in venture capital. Information Sciences, 562, 347–369.

Torkayesh, A.E., Pamucar, D., Ecer, F., Chatterjee, P. (2021). An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. Socio-Economic Planning Sciences, 78, 101052.

Turskis, Z., Bausys, R., Smarandache, F., Kazakeviciute-Januskeviciene, G., Zavadskas, E.K. (2022). M-generalised q-neutrosophic extension of CoCoSo method. International Journal of Computers Communications & Control, 17(1), 4646.

Verma, R., Alvarez-Miranda, E. (2023). Group decision-making method based on advanced aggregation operators with entropy and divergence measures under 2-tuple linguistic Pythagorean fuzzy environment. Expert Systems with Applications, 231, 120584.

Wang, M., Liu, K., Zhao, S.H. (2013). Evaluation of electric vehicle charging station sitting based on fuzzy analytic hierarchy process. In: 2013 Fourth International Conference on Digital Manufacturing and Automation (ICDMA). Shinan, China, 2013, pp. 568–571. https://doi.org/10.1109/ICDMA.2013.134.

Wang, J.M., Liu, Y., Yang, Y.F., Cai, W., Wang, D.X., Jia, Z.W. (2019). The location of electric vehicle charging stations based on FRLM with robust optimization. International Journal of Pattern Recognition and Artificial Intelligence, 33(8), 1959027.

Wang, Y., Tang, K.W., Lai, K.X., Chen, T., Wu, X. (2021). Optimal planning of charging stations for an electric vehicle fleet in car-sharing business. International Transactions on Electrical Energy Systems, 31(11), e13098.

Wang, Y., Zhou, J.X., Sun, Y.Y., Wang, X.W., Zhe, J.Y., Wang, H.Z. (2022). Electric vehicle charging station location-routing problem with time windows and resource sharing. Sustainability, 14(18), 11681.

Wei, G.W., Lin, R., Lu, J.P., Wu, J., Wei, C. (2022). The generalized dice similarity measures for probabilistic uncertain linguistic MAGDM and its application to location planning of electric vehicle charging stations. International Journal of Fuzzy Systems, 24, 933–948.

Wu, P., Li, F.G., Zhao, J., Zhou, L.G., Martfnez, L. (2023). Consensus reaching process with multiobjective optimization for large-scale group decision making with cooperative game. IEEE Transactions on Fuzzy Systems, 31(1), 293–306.

Xu, X.X., Gong, Z.W., Herrera-Viedma, E., Kou, G., Cabrerizo, F.J. (2023). Consensus reaching in group decision making with linear uncertain preferences and asymmetric costs. IEEE Transactions on Systems Man Cybernetics-Systems, 53(5), 2887–2899.

Yager, R.R., Abbasov, A.M. (2013). Pythagorean membership grades, complex numbers, and decision making. International Journal of Intelligent Systems, 28(5), 436–452.

Yang, R.Z., Cao, Q.N. (2019). Time-satisfaction of data series based on computer original genetic algorithm gradually covers the location and algorithm of electric vehicle charging station. Journal of Intelligent & Fuzzy Systems, 37(5), 5993–6001.

Yazdani, M., Zarate, P., Zavadskas, E.K., Turskis, Z. (2018). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519.

Yazdani, M., Wen, Z., Liao, H.C., Banaitis, A., Turskis, Z. (2019). A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. Journal of Civil Engineering and Management, 25(8), 858–874.

Yazdekhasti, A., Jazi, M.A., Ma, J.F. (2021). Electric vehicle charging station location determination with consideration of routing selection policies and driver’s risk preference. Computers & Industrial Engineering, 162, 107674.

Ye, J. (2016). Similarity measures of intuitionistic fuzzy sets based on cosine function for the decision making of mechanical design schemes. Journal of Intelligent & Fuzzy Systems, 30(1), 151–158.

Yi, T., Cheng, X.B., Zheng, H., Liu, J.P. (2019). Research on location and capacity optimization method for electric vehicle charging stations considering user’s comprehensive satisfaction. Energies, 12(10), 1915.

Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

Zhang, H.M., Dai, Y.Y. (2023). Consensus improvement model in group decision making with hesitant fuzzy linguistic term sets or hesitant fuzzy linguistic preference relations. Computers & Industrial Engineering, 178, 14.

Zhang, H., Shi, F.F. (2023). A multi-objective site selection of electric vehicle charging station based on NSGA-II. International Journal of Industrial Engineering Computations, 15, 293–306.

Zhang, H.Y., Wei, G.W. (2023). Location selection of electric vehicles charging stations by using the spherical fuzzy CPT-CoCoSo and D-CRITIC method. Computational and Applied Mathematics, 42(60). https://doi.org/10.1007/s40314-022-02183-9.

Zhang, Z.H., Huang, Q.X., Huang, C., Yuan, X.G., Zhang, D.W. (2014). The layout optimization of charging stations for electric vehicles based on the chaos particle swarm algorithm. In: 6th Chinese Conference on Pattern Recognition (CCPR), Changsha, China, 2014. Springer-Verlag, Berlin, pp. 565–574.

Zhang, H., Wei, G., Chen, X. (2022). SF-GRA method based on cumulative prospect theory for multiple attribute group decision making and its application to emergency supplies supplier selection. Engineering Applications of Artificial Intelligence, 110, 104679.

Zhang, H.Y., Wang, H.J., Wei, G.W. (2023). Spherical fuzzy TODIM method for MAGDM integrating cumulative prospect theory and CRITIC method and its application to commercial insurance selection. Artificial Intelligence Review, 56, 10275–10296.

Zhao, H., Gao, J., Cheng, X. (2023). Electric vehicle solar charging station siting study based on GIS and multi-criteria decision-making: a case study of China. Sustainability, 15(14), 10967. https://doi.org/10.3390/su151410967.

Zu, S.D., Sun, L.J. (2022). Research on location planning of urban charging stations and battery-swapping stations for electric vehicles. Energy Reports, 8, 508–522.

Biographies

Yan Rong

R. Yan was born in Hubei in 1985, and graduated from Hainan Tropical Ocean University with a master’s degree. Now, he works as a teacher at Chongqing City Vocational College. His main research fields are tourism planning and business administration.

Han Yongguang

Y. Han was born in Shandong in 1982, graduated with a master’s degree from Chongqing Normal University and is now works as a teacher at Chongqing City Vocational College. The main research areas are scenic spot planning and project management.

Zhang Huiyuan

H. Zhang currently is a PhD student at the School of Mathematical Sciences, Sichuan Normal University, Chengdu, 610066, PR. China. She is currently interested in aggregation operators, decision making and computing with words.

Wei Cun

weicun1990@163.com