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Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station
Author(s) -
Mishra Arunodaya Raj,
Rani Pratibha,
Saha Abhijit
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22523
Subject(s) - selection (genetic algorithm) , measure (data warehouse) , multiple criteria decision analysis , computer science , similarity (geometry) , process (computing) , electric vehicle , sensitivity (control systems) , similarity measure , charging station , data mining , artificial intelligence , operations research , machine learning , mathematics , engineering , power (physics) , physics , quantum mechanics , image (mathematics) , electronic engineering , operating system
Abstract Sustainable site selection for electric vehicle charging station (EVCS) is a significant process in the promotion of electric vehicle system development. The assessment and selection of suitable EVCS site is a very critical decision, involving complexity due to the presence of several associated criteria. Furthermore, uncertainty is an inevitable component of the information in the decision‐making procedure and its significance in the selection process is relatively high and needs to be cautiously measured. Single‐valued neutrosophic set (SVNS) is one of the valuable and flexible tools for handling such type of uncertain information arising in multi‐criteria decision‐making (MCDM) applications. Thus, the objective of this study is to introduce novel single‐valued neutrosophic information‐based additive ratio assessment (ARAS) approach for evaluating and prioritizing the sustainable EVCS sites. In this method, novel single‐valued subjective and objective weighted integrated approach (SVN‐SOWIA) is developed to compute the criteria by aggregating the objective weights resulted from a similarity measure‐based procedure and the subjective weights given by the experts. For this purpose, an innovative similarity measure is proposed for SVNSs. To display the performance of the present methodology, a computational study of EVCS sites evaluation is conferred under single‐valued neutrosophic environment. Comparative and sensitivity analyses are further performed to verify the strength of the developed approach. The outcome illustrates EVCS site EvUrjaa—Electric Vehicle Charging Station is the most optimal EVCS site in Indore region, India. Also, the environmental (0.324) and social (0.273) criteria are more important than technological (0.236) and economical (0.167) criteria in assessing the EVCS sites. The sensitivity analysis outcomes signify the EVCS option EvUrjaa—Electric Vehicle Charging Station always acquires its highest ranking in spite of how sub‐criteria weights fluctuate. The outcome of this study indicates that the developed approach can suggest more realistic performance under uncertain environment and therefore, provides a wide range of applications.