Advertisement

Location selection of electric vehicles charging stations by using a fuzzy MCDM method: a case study in Turkey

  • Ali Karaşan
  • İhsan KayaEmail author
  • Melike Erdoğan
Original Article

Abstract

Pollution, climate change, fast natural resource depletion, deforestation and global warming have become major worldwide problems relevant with the petroleum-based powered vehicles and alternatives for this conventional transportation type have been started to change in the last decade. In this modification process, electric vehicles (EVs) have a leading position due to their low damage effect to the environment. Selecting the most sustainable location for charging station for EVs plays an important role in the life cycle of them. This process needs to consider some conflicting criteria and has a complex decision problem that can be modeled as a multi-criteria decision-making problem. The inclusion of such criteria in a location selection requires the fuzzy sets to be used in the decision-making methodology. For this aim, intuitionistic fuzzy sets have been used in this paper. By the way, a decision-making procedure based on intuitionistic fuzzy sets and consists of the decision-making trial and evaluation laboratory, analytic hierarchy process and technique for order preference by similarity to ideal solution has been suggested for the location selection of charge stations. The proposed fuzzy-based model is applied to a case study for Istanbul in Turkey.

Keywords

Electric vehicles charging stations Location selection Intuitionistic fuzzy sets Decision making DEMATEL AHP TOPSIS 

Notes

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Eisel M, Schmidt J, Kolbe LM (2014) Finding suitable locations for charging stations. In: 2014 IEEE international electric vehicle conference (IEVC). IEEE, pp 1–8Google Scholar
  2. 2.
    Traut E, Hendrickson C, Klampfl E, Liu Y, Michalek JJ (2012) Optimal design and allocation of electrified vehicles and dedicated charging infrastructure for minimum life cycle greenhouse gas emissions and cost. Energy Policy 51:524–534CrossRefGoogle Scholar
  3. 3.
    Islam MM, Shareef H, Mohamed A (2015) Optimal quick charging station placement for electric vehicles. Appl Mech Mate 785:697–701CrossRefGoogle Scholar
  4. 4.
    Lam AY, Leung YW, Chu X (2014) Electric vehicle charging station placement: formulation, complexity, and solutions. IEEE Trans Smart Grid 5(6):2846–2856CrossRefGoogle Scholar
  5. 5.
    Yi Z, Bauer PH (2014) Energy consumption model and charging station placement for electric vehicles. In: 3rd International conference on smart grids and green IT systems, pp 150–156Google Scholar
  6. 6.
    Liu Z, Wen F, Ledwich G (2013) Optimal planning of electric-vehicle charging stations in distribution systems. IEEE Trans Power Deliv 28(1):102–110CrossRefGoogle Scholar
  7. 7.
    Kong C, Jovanovic R, Bayram IS, Devetsikiotis M (2017) A hierarchical optimization model for a network of electric vehicle charging stations. Energies 10(5):675CrossRefGoogle Scholar
  8. 8.
    Xu Q, Cai T, Liu Y (2016) Location planning of charging stations for electric vehicles based on drivers behaviors’ and travel chain. Autom Electric Power Syst 4:59–65Google Scholar
  9. 9.
    Jia L, Hu Z, Song Y, Luo Z (2012) Optimal siting and sizing of electric vehicle charging stations. In: 2012 IEEE international on electric vehicle conference (IEVC). IEEE, pp 1–6Google Scholar
  10. 10.
    Tabari M, Kaboli A, Aryanezhad MB, Shahanaghi K, Siadat A (2008) A new method for location selection: a hybrid analysis. Appl Math Comput 206(2):598–606MathSciNetzbMATHGoogle Scholar
  11. 11.
    Zadeh LA (1965) Fuzzy set. Inf Control 8:338–353CrossRefGoogle Scholar
  12. 12.
    Erdoǧan M, Bilisik ON, Kaya I (2018) A new fuzzy decision-making procedure to prioritization of the brand city candidates for Turkey. J Mult Valued Logic Soft Comput 30(1):1–28Google Scholar
  13. 13.
    Kaya İ, Erdoğan M, Yıldız C (2017) Analysis and control of variability by using fuzzy individual control charts. Appl Soft Comput 51:370–381CrossRefGoogle Scholar
  14. 14.
    Kaya İ (2014) The process incapability index under fuzziness with an application for decision making. Int J Comput Intell Syst 7(1):114–128CrossRefGoogle Scholar
  15. 15.
    Kaya İ (2012) Evaluation of outsourcing alternatives under fuzzy environment for waste management. Resour Conserv Recycl 60:107–118CrossRefGoogle Scholar
  16. 16.
    Özkan B, Kaya I, Başligil H (2017) A fuzzy based goal programming methodology for minimizing the risk factors: a real case application in pharmaceutical sector. J Multip Valued Logic Soft Comput 28(4–5):475–493MathSciNetGoogle Scholar
  17. 17.
    Parchami A, Ivani R, Mashinchi M, Kaya İ (2017) An implication of fuzzy ANOVA: metal uptake and transport by corn grown on a contaminated soil. Chemometr Intell Lab Syst 164:56–63CrossRefGoogle Scholar
  18. 18.
    Hui W (2013) Some operations on interval-valued intuitionistic fuzzy sets. In: 2013 Fifth international conference on computational and information sciences (ICCIS). IEEE, pp 832–834Google Scholar
  19. 19.
    Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96CrossRefGoogle Scholar
  20. 20.
    Zhao T, Xiao J (2012) Type-2 intuitionistic fuzzy sets. Control Theory Appl 29(9):1215–1222Google Scholar
  21. 21.
    Gabus A, Fontela E (1972) World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Center, Geneva, pp 1–8Google Scholar
  22. 22.
    Si SL, You XY, Liu HC, Zhang P (2018) DEMATEL technique: a systematic review of the state-of-the-art literature on methodologies and applications. Math Probl EngGoogle Scholar
  23. 23.
    Erdogan M, Kaya I (2015) An integrated multi-criteria decision-making methodology based on type-2 fuzzy sets for selection among energy alternatives in Turkey. Iran J Fuzzy Syst 12(1):1–25MathSciNetGoogle Scholar
  24. 24.
    Wang Y-W, Lin C-C (2009) Locating road-vehicle refueling stations. Transp Res Part E 45:821–829CrossRefGoogle Scholar
  25. 25.
    Pan F, Bent R, Berscheid A, Izraelevitz D (2010) Locating PHEV exchange stations in V2G. In: 2010 First IEEE international conference on smart grid communications (SmartGridComm). IEEE, pp 173–178Google Scholar
  26. 26.
    Schill WP (2011) Electric vehicles in imperfect electricity markets: the case of Germany. Energy Policy 39(10):6178–6189CrossRefGoogle Scholar
  27. 27.
    Kley F, Lerch C, Dallinger D (2011) New business models for electric cars—a holistic approach. Energy Policy 39(6):3392–3403CrossRefGoogle Scholar
  28. 28.
    Wirges J, Linder S, Kessler A (2012) Modelling the development of a regional charging infrastructure for electric vehicles in time and space. Eur J Transp Infrastruct Res 12:391–416Google Scholar
  29. 29.
    Andrews M, Dogru MK, Hobby JD, Jin Y, Tucci H (2013) Modeling and optimization for electric vehicle charging infrastructure. In: IEEE innovative smart grid technologies conferenceGoogle Scholar
  30. 30.
    Pazouki S, Mohsenzadeh A, Haghifam MR (2013) Optimal planning of PEVs charging stations and demand response programs considering distribution and traffic networks. In: 2013 Smart grid conference (SGC). IEEE, pp 90–95Google Scholar
  31. 31.
    He F, Wu D, Yin Y, Guan Y (2013) Optimal deployment of public charging stations for plug-in hybrid electric vehicles. Transp Res Part B Methodol 47:87–101CrossRefGoogle Scholar
  32. 32.
    Lam A, Leung YW, Chu X (2013) Electric vehicle charging station placement. In: 2013 IEEE international conference on smart grid communications (SmartGridComm). IEEE, pp 510–515Google Scholar
  33. 33.
    Wagner S, Götzinger M, Neumann D (2013) Optimal location of charging stations in smart cities: a point of interest based approach. In: 34th International conference on information systems (ICIS), MilanGoogle Scholar
  34. 34.
    Micari S, Napoli G, Antonucci V, Andaloro L (2014) Electric vehicles charging stations network—a preliminary evaluation about Italian highways. In: 2014 IEEE international electric vehicle conference (IEVC). IEEE, pp 1–5Google Scholar
  35. 35.
    Zhenghui Z, Qingxiu H, Chun H, Xiuguang Y, Zhang D (2014) The layout optimization of charging stations for electric vehicles based on the chaos particle swarm algorithm. In: Chinese conference on pattern recognition. Springer, Berlin, pp 565–574Google Scholar
  36. 36.
    Sadeghi-Barzani P, Rajabi-Ghahnavieh A, Kazemi-Karegar H (2014) Optimal fast charging station placing and sizing. Appl Energy 125:289–299CrossRefGoogle Scholar
  37. 37.
    Meng W, Kai L (2017) Location of electric vehicle charging station based on spatial clustering and multi-hierarchical fuzzy evaluation. Trans Nanjing Univ Aeronaut Astronaut 1:013Google Scholar
  38. 38.
    Wei G, Wang X (2007) Some geometric aggregation operators based on interval-valued intuitionistic fuzzy sets and their application to group decision making. IEEE, Harbin, pp 495–499Google Scholar
  39. 39.
    Abdullah L, Ismail WKW (2012) Hamming distance in intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets: a comparative analysis. Adv Comput Math Appl 1(1):7–11Google Scholar
  40. 40.
    Karaşan A, Kahraman C (2017) A novel intuitionistic fuzzy DEMATEL–ANP–TOPSIS integrated methodology for freight village location selection. J Intell Fuzzy Syst 1–18 (Preprint)Google Scholar
  41. 41.
    Kwong CK, Bai H (2003) Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach. IIE Trans 35(7):619–626CrossRefGoogle Scholar
  42. 42.
    Lee AH, Chen WC, Chang CJ (2008) A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Syst Appl 34(1):96–107CrossRefGoogle Scholar
  43. 43.
    Wang L, Chu J, Wu J (2007) Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process. Int J Prod Econ 107(1):151–163CrossRefGoogle Scholar
  44. 44.
    Kulak O, Kahraman C (2005) Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process. Inf Sci 170(2–4):191–210CrossRefGoogle Scholar
  45. 45.
    Kwong CK, Bai H (2002) A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. J Intell Manuf 13(5):367–377CrossRefGoogle Scholar
  46. 46.
    Dong Y, Zhang G, Hong WC, Yu S (2013) Linguistic computational model based on 2-tuples and intervals. IEEE Trans Fuzzy Syst 21(6):1006–1018CrossRefGoogle Scholar
  47. 47.
    Dong Y, Xu Y, Yu S (2009) Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model. IEEE Trans Fuzzy Syst 17(6):1366–1378CrossRefGoogle Scholar
  48. 48.
    Herrera-Viedma E, López-Herrera AG (2007) A model of an information retrieval system with unbalanced fuzzy linguistic information. Int J Intell Syst 22(11):1197–1214CrossRefGoogle Scholar
  49. 49.
    Herrera F, Herrera-Viedma E, Martínez L (2008) A fuzzy linguistic methodology to deal with unbalanced linguistic term sets. IEEE Trans Fuzzy Syst 16(2):354–370CrossRefGoogle Scholar
  50. 50.
    Li CC, Dong Y, Herrera F, Herrera-Viedma E, Martínez L (2017) Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching. Inf Fusion 33:29–40CrossRefGoogle Scholar
  51. 51.
    Pedrycz W, Song M (2014) A granulation of linguistic information in AHP decision-making problems. Inf Fusion 17:93–101CrossRefGoogle Scholar
  52. 52.
    Wu J, Huang HB, Cao QW (2013) Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multi-criteria decision making problems. Appl Math Model 37(24):9898–9906MathSciNetCrossRefGoogle Scholar
  53. 53.
    Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98MathSciNetGoogle Scholar
  54. 54.
    Park JH, Park IY, Kwun YC, Tan X (2011) Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment. Appl Math Model 35(5):2544–2556MathSciNetCrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Institute of Natural and Applied SciencesYildiz Technical UniversityDavutpaşa, IstanbulTurkey
  2. 2.Department of Industrial EngineeringYıldız Technical UniversityYıldız, Beşiktaş, IstanbulTurkey

Personalised recommendations