In this study, a multi-criteria group decision making (MCGDM) framework is constructed for electric vehicle fast-charging station (EVFCS) selection using a proportional hesitant fuzzy set (PHFS) that can describe two aspects of information: the possible membership degrees in the hesitant fuzzy elements and associated proportion representing statistical information from different groups. A newly extended distance measure for PHFSs is introduced and an extended maximizing deviation method is constructed to obtain criteria weights objectively. Accordingly, an integrated PHFS-VIKOR (VlseKriterijum-ska Optimizacija I Kompromisno Resenje) method embedded with a new distance measure and extended maximizing deviation method is presented. With increasing concerns about range anxiety, it is essential to seek an optimal location for EVFCS considering efficient utilization of resources and long-term development of socio-economy under proportional hesitant fuzzy environment. Lastly, an illustration with sensitivity analysis and comparative analyses is provided to demonstrate the validity and robustness of our proposal.
NiuL., ZhangP. and WangX., Hierarchical power control strategy on small-scale electric vehicle fast charging station, Journal of Cleaner Production199 (2018), 1043–1049.
2.
HeY., KockelmanK.M. and PerrineK.A., Optimal locations of US fast charging stations for long-distance trip completion by battery electric vehicles, Journal of Cleaner Production214 (2019), 452–461.
3.
YuanK., SongY., ShaoY., SunC. and WuZ., A charging strategy with the price stimulus considering the queue of charging station and ev fast charging demand, Energy Procedia145 (2018), 400–405.
4.
CaiH., JiaX., ChiuA.S.F., HuX. and XuM., Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet, Transportation Research Part D: Transport and Environment33 (2014), 39–46.
5.
KimJ., SonS., LeeJ.M. and HaH.T., Scheduling and performance analysis under a stochastic model for electric vehicle charging stations, Omega66 (2017), 278–289.
6.
YangQ., SunS., DengS., ZhaoQ. and ZhouM., Optimal sizing of PEV fast charging stations with Markovian demand characterization, IEEE Transactions on Smart Grid10 (2018), 4457–4466.
7.
JuY., JuD., GonzalezE.D.R.S., GiannakisM. and WangA., Study of site selection of electric vehicle charging station based on extended GRP method under picture fuzzy environment, Comput Ind Eng135 (2019), 1271–1285.
8.
XuJ., ZhongL., YaoL. and WuZ., An interval type-2 fuzzy analysis towards electric vehicle charging station allocation from a sustainable perspective, Sustainable Cities And Society40 (2018), 335–351.
9.
LiuH., YangM., ZhouM. and TianG., An integrated multi-criteria decision making approach to location planning of electric vehicle charging stations, IEEE Transactions on Intelligent Transportation Systems20 (2018), 362–373.
10.
ErbaşM., KabakM., ÖzceylanE. and ÇetinkayaC., Optimal siting of electric vehicle charging stations: A GIS-based fuzzy multi-criteria decision analysis, Energy163 (2018), 1017–1031.
11.
WangL., WangX.-k., PengJ.-j. and WangJ.-q., The differences in hotel selection among various types of travellers: A comparative analysis with a useful bounded rationality behavioural decision support model, Tourism Management76 (2020).
12.
TianC., PengJ.-j., ZhangW.-y., ZhangS. and WangJ.-q., Tourism environmental impact assessment based on improved AHP and picture fuzzy PROMETHEE II methods, Technological and Economic Development of Economy (2019), 1–24.
13.
TorraV., Hesitant fuzzy sets, International Journal of Intelligent Systems25 (2010), 529–539.
14.
Z. B, Decision method for research and application based on preference relation., Southeast University, Nanjing, 2014.
15.
ZhangZ. and WuC., Weighted hesitant fuzzy sets and their application to multi-criteria decision making, Journal of Advances in Mathematics and Computer Science (2014), 1091–1123.
16.
XiongS., ChenZ. and ChinK., A novel MAGDM approach with proportional hesitant fuzzy sets, International Journal of Computational Intelligence Systems11 (2018), 256–271.
17.
ChenZ.-S., ChinK.-S., LiY.-L. and YangY., Proportional hesitant fuzzy linguistic term set for multiple criteria group decision making, Information Sciences357 (2016), 61–87.
18.
ChenZ.-S., ChinK.-S., MuN.-Y., XiongS.-H., ChangJ.-P. and YangY., Generating HFLTS possibility distribution with an embedded assessing attitude, Information Sciences394–395 (2017), 141–166.
19.
ChenZ.-S., ChinK.-S., MartinezL. and TsuiK.-L., Customizing semantics for individuals with attitudinal HFLTS possibility distributions, IEEE Transactions on Fuzzy Systems26 (2018), 3452–3466.
20.
ChenZ.-S., YangY., WangX.-J., ChinK.-S. and TsuiK.-L., Fostering linguistic decision-making under uncertainty: A proportional interval type-2 hesitant fuzzy TOPSIS approach based on Hamacher aggregation operators and andness optimization models, Information Sciences500 (2019), 229–258.
21.
ChenZ.S., LiM., KongW.T. and ChinK.S., Evaluation and selection of hazmat transportation alternatives: A PHFLTS- and TOPSIS-integrated multi-perspective approach, International Journal of Environmental Research and Public Health16 (2019).
22.
PangQ., WangH. and XuZ., Probabilistic linguistic term sets in multi-attribute group decision making, Information Sciences369 (2016), 128–143.
23.
TianZ., NieR. and WangJ., Probabilistic linguistic multicriteria decision-making based on evidential reasoning and combined ranking methods considering decisionmakers’ psychological preferences, Journal of the Operational Research Society10.1080/01605682.2019.1632752 (2019).
24.
LiuP. and TengF., Probabilistic linguistic TODIM method for selecting products through online product reviews, Information Sciences485 (2019), 441–455.
25.
LiuP. and LiY., An extended MULTIMOORA method for probabilistic linguistic multi-criteria group decision-making based on prospect theory, Comput Ind Eng136 (2019), 528–545.
26.
LiuP. and LiY., A novel decision-making method based on probabilistic linguistic information, Cognitive Computation (2019), 1–13.
27.
LiuP., LiY. and TengF., Bidirectional projection method for probabilistic linguistic multi-criteria group decision-making based on power average operator, International Journal of Fuzzy Systems10.1007/s40815-019-00705-y (2019).
28.
LiuP. and TengF., Some Muirhead mean operators for probabilistic linguistic term sets and their applications to multiple attribute decision making, Applied Soft Computing68 (2018), 396–431.
29.
LiuP. and LiY., The PROMTHEE II method based on probabilistic linguistic information and their application to decision making, Informatica29 (2018), 303–320.
30.
LuoS., ZhangH., WangJ. and LiL., Group decision-making approach for evaluating the sustainability of constructed wetlands with probabilistic linguistic preference relations, Journal of the Operational Research Society10.1080/01605682.2018.1510806 (2019).
31.
XiaoF. and WangJ., Multistage decision support framework for sites selection of solar power plants with probabilistic linguistic information, Journal of Cleaner Production230 (2019), 1396–1409.
32.
DeyB., BairagiB., SarkarB. and SanyalS.K., Group heterogeneity in multi member decision making model with an application to warehouse location selection in a supply chain, Comput Ind Eng105 (2017), 101–122.
33.
GargH. and KaurG., Algorithm for probabilistic dual hesitant fuzzy multi-criteria decision-making based on aggregation operators with new distance measures, Mathematics6 (2018), 280.
34.
ZhangS., XuZ. and HeY., Operations and integrations of probabilistic hesitant fuzzy information in decision making, Information Fusion38 (2017), 1–11.
35.
SongC., XuZ. and ZhaoH., A novel comparison of probabilistic hesitant fuzzy elements in multi-criteria decision making, Symmetry10 (2018), 177.
36.
ZhangG., WangJ. and WangT., Multi-criteria group decision-making method based on TODIM with probabilistic interval-valued hesitant fuzzy information, Expert Systems36 (2019).
37.
TianX., XuZ. and FujitaH., Sequential funding the venture project or not? A prospect consensus process with probabilistic hesitant fuzzy preference information, Knowledge-Based Systems161 (2018), 172–184.
38.
ZhouW. and XuZ., Group consistency and group decision making under uncertain probabilistic hesitant fuzzy preference environment, Information Sciences414 (2017), 276–288.
39.
LiJ. and WangJ., Multi-criteria decision-making with probabilistic hesitant fuzzy information based on expected multiplicative consistency, Neural Computing and Applications10.1007/s00521-018-3753-1 (2018).
40.
WuJ., LiuX.-D., WangZ.-W. and ZhangS.-T., Dynamic emergency decision-making method with probabilistic hesitant fuzzy information based on GM (1, 1) and TOPSIS, IEEE Access7 (2018), 7054–7066.
41.
DingJ., XuZ. and ZhaoN., An interactive approach to probabilistic hesitant fuzzy multi-attribute group decision making with incomplete weight information, Journal of Intelligent & Fuzzy Systems32 (2017), 2523–2536.
42.
LiJ. and WangJ.Q., Multi-criteria outranking methods with hesitant probabilistic fuzzy sets, Cognitive Computation9 (2017), 611–625.
43.
OpricovicS., Multicriteria optimization of civil engineering systems, Faculty of Civil Engineering, Belgrade2 (1998), 5–21.
44.
ChenT., Remoteness index-based Pythagorean fuzzy VIKOR methods with a generalized distance measure for multiple criteria decision analysis, Information Fusion41 (2018), 129–150.
45.
MousaviS.M., VahdaniB. and BehzadiS.S., Designing a model of intuitionistic fuzzy VIKOR in multi-attribute group decision-making problems, Iranian Journal of Fuzzy Systems13 (2016), 45–65.
46.
MiX., LiJ., LiaoH., ZavadskasE.K., Al-BarakatiA., BarnawiA., TaylanO. and Herrera-ViedmaE., Hospitality brand management by a score-based q-rung orthopair fuzzy VIKOR method integrated with the best worst method, Economic Research-Ekonomska Istraživanja32 (2019), 3266–3295.
47.
EmeçŞ. and AkkayaG., Stochastic AHP and fuzzy VIKOR approach for warehouse location selection problem, Journal of Enterprise Information Management31 (2018), 950–962.
48.
WuZ.B., AhmadJ. and XuJ.p., A group decision making framework based on fuzzy VIKOR approach for machine tool selection with linguistic information, Applied Soft Computing42 (2016), 314–324.
49.
LiuH.C., YouJ.X., YouX.Y. and ShanM.M., A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method, Applied Soft Computing28 (2015), 579–588.
50.
WangL., ZhangH.Y., WangJ.Q. and LiL., Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project, Applied Soft Computing64 (2018), 216–226.
51.
ShenK.W. and WangJ.Q., Z-VIKOR method based on a new weighted comprehensive distance measure of Z-number and its application, IEEE Transactions on Fuzzy Systems10.1109/tfuzz.2018.2816581 (2018), 1–1.
52.
JiangY., LiangH. and SunM., A method for discrete stochastic MADM problems based on the ideal and nadir solutions, Comput Ind Eng87 (2015), 114–125.
53.
OpricovicS. and TzengG.H., Multicriteria planning of post earthquake sustainable reconstruction, Computer Aided Civil and Infrastructure Engineering17 (2002), 211–220.
54.
OpricovicS. and TzengG.-H., Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS, European Journal of Operational Research156 (2004), 445–455.
55.
WuY., YangM., ZhangH., ChenK. and WangY., Optimal site selection of electric vehicle charging stations based on a cloud model and the PROMETHEE method, Energies9 (2016), 157.
56.
GuoS. and ZhaoH., Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective, Applied Energy158 (2015), 390–402.
57.
LiaoH. and XuZ., A VIKOR-based method for hesitant fuzzy multi-criteria decision making, Fuzzy Optimization and Decision Making12 (2013), 373–392.
