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All issues Volume 405 (2023) E3S Web of Conf., 405 (2023) 02005 References
Open Access
Issue
E3S Web of Conf.
Volume 405, 2023
2023 International Conference on Sustainable Technologies in Civil and Environmental Engineering (ICSTCE 2023)
Article Number 02005
Number of page(s) 15
Section Renewable Energy & Electrical Technology
DOI https://doi.org/10.1051/e3sconf/202340502005
Published online 26 July 2023
  1. F.Ecer., A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews, 143(January). (2021). https://doi.org/10.1016/j.rser.2021.110916 [CrossRef] [Google Scholar]
  2. G.Van De Kaa ., D.Scholten ., J.Rezaei ., & Milchram C. The battle between battery and fuel cell powered electric vehicles: A BWM approach. Energies, 10(11). (2017). https://doi.org/10.3390/en10111707 [CrossRef] [Google Scholar]
  3. N.C.Onat ., M.Noori ., M.Kucukvar .,Y. Zhao ., O.Tatari ., & M.Chester., Exploring the suitability of electric vehicles in the United States. Energy, 121(February), 631–642. . (2017). https://doi.org/10.1016/j.energy.2017.01.035 [CrossRef] [Google Scholar]
  4. G Bucsan., M Balchanos., D. N Mavris., J. S Lee., M Ishigaki., & A Iwai. , Management of technologies for electric vehicle efficiency towards optimizing range. 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, 3836–3841. (2017).https://doi.org/10.1109/SMC.2016.7844832 [Google Scholar]
  5. S. N Kane., A Mishra., & A. K Dutta., Preface: International Conference on Recent Trends in Physics (ICRTP 2016). Journal of Physics: Conference Series, 755(1). (2016). https://doi.org/10.1088/1742-6596/755/1/011001 [Google Scholar]
  6. R. B De Souza., & F. G Dedini., Energy management strategy for hybrid electric vehicles. SAE Technical Papers, 1–4. (2009). https://doi.org/10.4271/2009-36-0328 [Google Scholar]
  7. A.R Domingues., P Marques., R Garcia., F Freire., L.C. Dias., Applying Multi-Criteria Decision Analysis to the Life-Cycle Assessment of vehicles. J. Clean. Prod. 2015, 107, 749–759, doi:10.1016/j.jclepro.2015.05.086 [CrossRef] [Google Scholar]
  8. G Domingues-Olavarría., Modeling, optimization and analysis of electromobility systems. (2018). [Google Scholar]
  9. R. Leirós-Rodrígueza, M.E. Arceb, C. Míguez-Álvarezc, J. L. G.-S. Article in Press Article in Press. Effect of Grain Boundaries on Paraconductivity of YBCO, 1(1), 1–11. (2016). [Google Scholar]
  10. N. C Onat., S Gumus., M Kucukvar., & O Tatari,. Application of the TOPSIS and intuitionistic fuzzy set approaches for ranking the life cycle sustainability performance of alternative vehicle technologies. Sustainable Production and Consumption, 6(December 2015), 12–25., (2016). https://doi.org/10.1016/j.spc.2015.12.003. [CrossRef] [Google Scholar]
  11. J. P Chang., Z. S Chen., X. L Liu., W. T Kong., S. H Xiong., & L.Martinez,. Paradigm Shift Toward Aggregation Strategies in Proportional Hesitant Fuzzy Multi-Criteria Group Decision Making Models of Advanced Practice for Selecting Electric Vehicle Battery Supplier. IEEE Access, 7, (2019),172534–172561. https://doi.org/10.1109/ACCESS.2019.2956393 [CrossRef] [Google Scholar]
  12. C Yang., Q Wang., W Peng., J. Zhang ., & J Zhu,. A normal wiggly pythagorean hesitant fuzzy bidirectional projection method and its application in EV power battery recycling mode selection. IEEE Access, 8, 62164–62180. ,(2020). https://doi.org/10.1109/ACCESS.2020.2984242 [CrossRef] [Google Scholar]
  13. D Wilken., M Oswald., P Draheim., C Pade., U Brand., & T Vogt., Multidimensional assessment of passenger cars: Comparison of electric vehicles with internal combustion engine vehicles. Procedia CIRP, 90, 291–296. (2020). https://doi.org/10.1016/j.procir.2020.01.101 [CrossRef] [Google Scholar]
  14. J Liu., & Q Dai,. Portfolio optimization of photovoltaic/battery energy storage/electric vehicle charging stations with sustainability perspective based on cumulative prospect theory and MOPSO. Sustainability (Switzerland), 12(3). (2020). https://doi.org/10.3390/su12030985 [Google Scholar]
  15. N Kishor., & J Fraile-Ardanuy,. ICT for electric vehicle integration with the smart grid. ICT for Electric Vehicle Integration with the Smart Grid, 1–426. (2020). https://doi.org/10.1049/PBTR016E [Google Scholar]
  16. P. K Tarei., P Chand., & H Gupta,. Barriers to the adoption of electric vehicles: Evidence from India. Journal of Cleaner Production, 291(April), 1–8. (2021). https://doi.org/10.1016/j.jclepro.2021.125847 [CrossRef] [Google Scholar]
  17. M. K Loganathan., B Mishra., C. M Tan., T Kongsvik., & R. N Rai,. Multi-criteria decision making (MCDM) for the selection of Li-ion batteries used in electric vehicles (EVs). Materials Today: Proceedings, 41(xxxx), 1073–1077. (2019). https://doi.org/10.1016/j.matpr.2020.07.179 [Google Scholar]
  18. R Wang., X Li., & C.Li,. Optimal selection of sustainable battery supplier for battery swapping station based on Triangular fuzzy entropy -MULTIMOORA method. Journal of Energy Storage, 34(February), 1–7. (2021)https://doi.org/10.1016/j.est.2020.102013 [Google Scholar]
  19. N. N. M Aboushaqrah., N. C Onat., M Kucukvar., A. M. S Hamouda,., A. O Kusakci., & B Ayvaz., Selection of alternative fuel taxis: a ybridized approach of life cycle sustainability assessment and multi-criteria decision making with neutrosophic sets. International Journal of Sustainable Transportation, 16(9), 833–846. (2022). https://doi.org/10.1080/15568318.2021.1943075 [CrossRef] [Google Scholar]
  20. X Ren,, S Sun., & R Yuan,. A study on selection strategies for battery electric vehicles based on sentiments, analysis, and the MCDM model. Mathematical Problems in Engineering, (2021). https://doi.org/10.1155/2021/9984343 [Google Scholar]
  21. Z Tian,. peng, H Liang,. ming, R Nie,. xin, X Wang,. kang & J.Wang qiang. Data-driven multi-criteria decision support method for electric vehicle selection. Computers and Industrial Engineering, 177(March), 2019–2024. (2023). https://doi.org/10.1016/j.cie.2023.109061 [Google Scholar]
  22. M Patil., & B. B. Majumdar, An investigation on the key determinants influencing electric two-wheeler usage in urban Indian context. Research in Transportation Business and Management, 43(June), 12–14. (2022). https://doi.org/10.1016/j.rtbm.2021.100693 [Google Scholar]
  23. M Patil., & B. B. Mujumdar,. Analysis of the Key Determinants of Electric Two-Wheeler Use in the Indian Context. Lecture Notes in Civil Engineering, 219, 47–60. (2022)https://doi.org/10.1007/978-981-16-8259-9_3 [CrossRef] [Google Scholar]
  24. Nayana. Electric Vehicle Charging with Battery Scheduling and Multicriteria Optimization using Genetic Algorithm. Journal of Electrical Engineering and Automation, 2(3), 123–128. (2021). https://doi.org/10.36548/jeea.2020.3.003 [CrossRef] [Google Scholar]
  25. A Ghosh., M Dey., S. P Mondal,., A Shaikh., A Sarkar., & B Chatterjee,. Selection of best E-Rickshaw-A green energy game changer: An application of AHP and TOPSIS method. Journal of Intelligent and Fuzzy Systems, 40(6), 11217–11230(2021). https://doi.org/10.3233/JIFS-202406 [CrossRef] [Google Scholar]
  26. C Yang., Q Wang., M Pan, J Hu., W Peng., J Zhang., & L Zhang. A linguistic Pythagorean hesitant fuzzy MULTIMOORA method for third-party reverse logistics provider selection of electric vehicle power battery recycling. Expert Systems with Applications, 198(July), 1–7. (2022). https://doi.org/10.1016/j.eswa.2022.116 A 808 [Google Scholar]
  27. A Bhuyan., A Tripathy., R. K Padhy,., & Gautam,. Evaluating the lithium-ion battery recycling industry in an emerging economy: A multi-stakeholder and multi-criteria decision-making approach. Journal of Cleaner Production, 331(January), 1–7. (2022). https://doi.org/10.1016/j.jclepro.2021.130007 [CrossRef] [Google Scholar]
  28. P. I Ekel,. International Journal of Electrical Power & Energy Systems Fuzzy set-based approach for grid integration and operation of ultra-fast charging electric buses. 138(June), 1–7. (2022) [Google Scholar]
  29. D Bhattacharjee,., Ghosh, T., Bhola, P., Martinsen, K., & Dan, P. (2022). Ecodesigning and improving performance of plugin hybrid electric vehicle in rolling terrain through multi-criteria optimisation of powertrain. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 236(5), 1019–1039. https://doi.org/10.1177/09544070211027531 [CrossRef] [Google Scholar]
  30. K Urošević., Z Gligorić., I Miljanović., C Beljić., & M Gligorić,. Novel methods in multiple criteria decision-making process (Mcrat and raps)— application in the mining industry. Mathematics, 9(16). (2021). https://doi.org/10.3390/math9161980 [Google Scholar]
  31. M.Gligorić , K Urošević., S.Lutovac, & D.Halilovi, Optimal Coal Supplier Selection for Thermal Power Plant Based on Mcrat Method. (2021). [Google Scholar]
  32. T. L Saaty,. Analytic Hierarchy Process. Encyclopedia of Biostatistics. (2005). https://doi.org/10.1002/0470011815.b2a4a002 [Google Scholar]
  33. S Belay., J Goedert., Woldesenbet, A., & Rokooei, S. (2022). AHP based multi criteria decision analysis of success factors to enhance decision making in infrastructure construction projects. Cogent Engineering, 9(1). https://doi.org/10.1080/23311916.2022.2043996 [CrossRef] [Google Scholar]
  34. D Kim,., & M.Kim, Hybrid Analysis of the Decision-Making Factors for Software Upgrade Based on the Integration of AHP and DEMATEL. Symmetry, 14(1). (2022). https://doi.org/10.3390/sym14010172 [Google Scholar]
  35. Anuradha, & S Gupta,. AHP-based multi-criteria decision-making for forest sustainability of lower Himalayan foothills in northern circle, India—a case study. Environmental Monitoring and Assessment, 194(12), 0–11. (2022). https://doi.org/10.1007/s10661-022-10510-0 [CrossRef] [Google Scholar]
  36. H Veisi., R Deihimfard., A Shahmohammadi., & Hydarzadeh Y. (2022). Application of the analytic hierarchy process (AHP) in a multi-criteria selection of agricultural irrigation systems. Agricultural Water Management, 267(9), 1–7. https://doi.org/10.1016/j.agwat.2022.107619 [CrossRef] [Google Scholar]
  37. D Diakoulaki., G Mavrotas., & L Papayannaki., D E T E R M I N I N G OBJECTIVE WEIGHTS IN M U L T I P L E CRITERIA PROBLEMS?: THE CRITIC M E T H O D. 22(7), 763–770. (1995). [Google Scholar]
  38. P. H Nguyen., J F Tsai., Y. C Hu,. & G. V. Ajay Kumar., A Hybrid Method of MCDM for Evaluating Financial Performance of Vietnamese Commercial Banks Under COVID-19 Impacts. Studies in Systems, Decision and Control, 382, 23–45. (2022). https://doi.org/10.1007/978-3-030-79610-5_2 [CrossRef] [Google Scholar]
  39. B Aksakal., A Ulutaş,. F Balo,. & D Karabasevic,. A New Hybrid MCDM Model for Insulation Material Evaluation for Healthier Environment. Buildings, 12(5), 1–21. (2022). https://doi.org/10.3390/buildings12050655 [CrossRef] [Google Scholar]
  40. I Petkovski,. I Mihajlovic,. & A Fedajev,. Hybrid CRITIC-TOPSIS model for prioritizing digitally developed countries in the light of energy indicators. IMCSM Proceedings, XVIII(1), 264–277. (2022). [Google Scholar]
  41. M Keshavarz-Ghorabaee,. M Amiri., E. K Zavadskas., Z Turskis,. & J Antucheviciene,. Determination of objective weights using a new method based on the removal effects of criteria (Merec). Symmetry, 13(4), 1–20. (2021). https://doi.org/10.3390/sym13040525 [Google Scholar]
  42. Raut, R. D., Bhasin, H. V., & Kamble, S. S. (2012). Supplier selection using integrated multi-criteria decision-making methodology. International Journal of Operational Research, 13(4), 359–394. https://doi.org/10.1504/IJOR.2012.046223 [CrossRef] [Google Scholar]
  43. G Shanmugasundar., G Sapkota., R Čep., & K Kalita,. Application of MEREC in Multi-Criteria Selection of Optimal Spray-Painting Robot. Processes, 10(6). (2022). https://doi.org/10.3390/pr10061172 [Google Scholar]
  44. B Ivanović., A Saha., Ž Stević., A Puška,. & E. K. Zavadskas,. Selection of truck mixer concrete pump using novel MEREC DNMARCOS model. Archives of Civil and Mechanical Engineering, 22(4), 1–22. (2022)https://doi.org/10.1007/s43452-022-00491-9 [CrossRef] [Google Scholar]
  45. S Ghosh,. M & Bhattacharya,. Analyzing the impact of COVID-19 on the financial performance of the hospitality and tourism industries: an ensemble MCDM approach in the Indian context. International Journal of Contemporary Hospitality Management, 34(8), 3113–3142. (2022). https://doi.org/10.1108/IJCHM-11-2021-1328 [CrossRef] [Google Scholar]
  46. U Ulas., Y Nese., & A Nuri,. Analysis of Efficiency and Productivity of Commercial Banks in Turkey Pre- and during COVID-19 with an Integrated MCDM Approach. Mathematics, 10(13), 2300. (2022). [CrossRef] [Google Scholar]
  47. N Fattouch., I Ben Lahmar., M Rekik,., & K Boukadi,. Decision-Making Approach for an IoRT-Aware Business Process Outsourcing. Digital, 2(4), 520–537. (2022). https://doi.org/10.3390/digital2040028 [CrossRef] [Google Scholar]
  48. A Arabameri., M Yamani., B Pradhan., A Melesse., K Shirani., & D Tien Bui,. Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility. Science of the Total Environment, 688, 903–916. (2019). https://doi.org/10.1016/j.scitotenv.2019.06.205 [CrossRef] [Google Scholar]
  49. H Musbah., G Ali., H. H Aly., & T. A Little,. Energy management using multi-criteria decision making and machine learning classification algorithms for intelligent system. Electric Power Systems Research, 203, 1–6. (2022). https://doi.org/10.1016/j.epsr.2021.107645 [CrossRef] [Google Scholar]
  50. P. R Srivastava., & P Eachempati,. Intelligent Employee Retention System for Attrition Rate Analysis and Churn Prediction: An Ensemble Machine Learning and Multi-Criteria Decision-Making Approach. Journal of Global Information Management, 29(6). (2021). https://doi.org/10.4018/JGIM.20211101.oa23 [Google Scholar]
  51. Q He., X Li.,D. W. N Kim., X Jia., X Gu., X Zhen., & L Zhou,. Feasibility study of a multi-criteria decision-making based hierarchical model for multi-modality feature and multi-classifier fusion: Applications in medical prognosis prediction. Information Fusion, 55(September 2019), 207–219. (2020). https://doi.org/10.1016/j.inffus.2019.09.001 [CrossRef] [Google Scholar]
  52. Q. B Pham., Y Achour., S. A Ali., F Parvin., M Vojtek., J Vojteková., N Al-Ansari., A. L Achu., R Costache., K. M Khedher., & D. T. Anh, .A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping. Geomatics, Natural Hazards and Risk, 12(1), 1741–1777. (2021). https://doi.org/10.1080/19475705.2021.1944330 [CrossRef] [Google Scholar]
  53. Z He., K. P Tran., S Thomassey., X Zeng., J Xu., & C Yi,. A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. Computers in Industry, 125. (2021). https://doi.org/10.1016/j.compind.2020.103373 [Google Scholar]
  54. M Kadkhodazadeh., M. V Anaraki., A Morshed-Bozorgdel., & S Farzin,. A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods. Sustainability (Switzerland), 14(5). (2022). https://doi.org/10.3390/su14052601 [Google Scholar]
  55. M. T Mustapha., D. U Ozsahin., I Ozsahin., & B Uzun,. Breast Cancer Screening Based on Supervised Learning and Multi-Criteria Decision-Making. Diagnostics, 12(6), 1–17. (2022). https://doi.org/10.3390/diagnostics12061326 [CrossRef] [PubMed] [Google Scholar]
  56. K Khosravi., H Shahabi., B. T Pham., J Adamowski., A Shirzadi., B Pradhan., J Dou., H. B Ly., G Gróf., H. L Ho., H Hong,. K Chapi., & I Prakash,. A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods. Journal of Hydrology, 573(November 2018), 311–323. (2019). https://doi.org/10.1016/j.jhydrol.2019.03.073 [CrossRef] [Google Scholar]
  57. Z Wang., J Li., G. P Rangaiah., & Z Wu,. Machine learning aided multi-objective optimization and multi-criteria decision making: Framework and two applications in chemical engineering. Computers and Chemical Engineering, 165(September), 1–8. (2022). https://doi.org/10.1016/j.compchemeng.2022.107945 [CrossRef] [Google Scholar]
  58. V García., J. S Sánchez., & A. I Marqués,. Synergetic application of multi-criteria decision-making models to credit granting decision problems. Applied Sciences (Switzerland), 9(23). (2019). https://doi.org/10.3390/app9235052 [Google Scholar]
  59. S. A Ali., F Parvi., J Vojteková., R Costache., N. T. T Linh., Q. B Pham., M Vojtek., L Gigović., A Ahmad., & M. A Ghorbani,. GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms. Geoscience Frontiers, 12(2), 857–876. (2021). https://doi.org/10.1016/j.gsf.2020.09.004 [CrossRef] [Google Scholar]
  60. N Pourkhodabakhsh., M. M Mamoudan., & A Bozorgi-Amiri,. Effective machine learning, Meta-heuristic algorithms and multi-criteria decision making to minimizing human resource turnover. Applied Intelligence. (2022). https://doi.org/10.1007/s10489-022-04294-6 [Google Scholar]
  61. N. K Chowdhury., M. A Kabir., & M. M Rahman,. An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classification. (2021). [Google Scholar]
  62. N. S Baqer., A. S Albahri., H. A Mohammed., A. A Zaidan., R. A Amjed., A. M Al-Bakry., O. S Albahri., H. A Alsattar., A Alnoor., A. H Alamoodi., B. B Zaidan., R. Q Malik., & Z. H Kareem,. Indoor air quality pollutants predicting approach using unified labelling process-based multi-criteria decision making and machine learning techniques. Telecommunication Systems, 81(4), 591–613. (2022). https://doi.org/10.1007/s11235-022-00959-2 [CrossRef] [Google Scholar]
  63. M. M Jassim., M. H Ali., A. S Elamer., M. M Jaber., M. Q Mohammed., & A Alkhayyat,. Multi-Criteria Decision Making for Machine Learning Algorithms Using AHP-VIKOR techniques: Case Study Adult Autism Diagnosis. IICETA 2022 - 5th International Conference on Engineering Technology and Its Applications, 574–578. (2022). https://doi.org/10.1109/IICETA54559.2022.9888273 [Google Scholar]
  64. A. J Al-Bawi., A. M Al-Abadi., B Pradhan,. & A. M. Alamri,. Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers. Geomatics, Natural Hazards and Risk, 12(1), 3035–3062. (2021). https://doi.org/10.1080/19475705.2021.1994024 [CrossRef] [Google Scholar]

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