A Multicriteria-Based Comparison of Electric Vehicles Using q-Rung Orthopair Fuzzy Numbers

Abstract

The subject of this research is the evaluation of electric cars and the choice of car that best meets the set research criteria. To this end, the criteria weights were determined using the entropy method with two-step normalization and a full consistency check. In addition, the entropy method was extended further with q-rung orthopair fuzzy (qROF) information and Einstein aggregation for carrying out decision making under uncertainty with imprecise information. Sustainable transportation was selected as the area of application. The current work compared a set of 20 leading EVs in India using the proposed decision-making model. The comparison was designed to cover two aspects: technical attributes and user opinions. For the ranking of the EVs, a recently developed multicriteria decision-making (MCDM) model, the alternative ranking order method with two-step normalization (AROMAN), was used. The present work is a novel hybridization of the entropy method, full consistency method (FUCOM), and AROMAN in an uncertain environment. The results show that the electricity consumption criterion (w = 0.0944) received the greatest weight, while the best ranked alternative was A7. The results also show robustness and stability, as revealed through a comparison with the other MCDM models and a sensitivity analysis. The present work is different from the past studies, as it provides a robust hybrid decision-making model that uses both objective and subjective information.

Multi-Criteria Selection of Electric Delivery Vehicles Using Fuzzy–Rough Methods

“…For this purpose, they used the AHP (Analytic Hierarchy Process), Neutrosophic sets, and TOPSIS methods. Biswas et al [39] addressed the possibility of reducing the impact on the environment and applying sustainability principles using electric vehicles. In their paper, they used the FUCOM (Full Consistency Method) and AROMAN (Alternative Ranking Order Method with Two-Step Normalization) approaches.…”

“…Various methods and procedures were used for the selection of electric vehicles in the abovementioned research papers. These selections were based mainly on the technical characteristics of vehicles [27][28][29][30]35,36], though linguistic evaluations [27,[29][30][31]33] and objective methods [35] were also used to determine the importance of criteria, while some papers used a combination of technical characteristics and expert linguistic assessments [39]. However, the fuzzy-rough approach has not been applied in the selection of such vehicles until now.…”

“…Based on this, this paper provides guidelines for developing the method and its application in MCDM problems. The methodology of using subjective ratings in the selection of electric vehicles is not new, since it was used in the paper of Biswas et al [39], but the use of the fuzzy-rough approach for such a selection is novel and represents a shift in practical applications. This approach opens up the possibility of further upgrading the decision-making process, so that it is possible to combine other approaches in future research.…”

Z-Numbers-Based MCDM Approach for Personnel Selection at Institutions of Higher Education for Transportation

“…However, none of the authors have used the AROMAN method for personnel selection, to date. To date, the AROMAN method has only been used a few times in other fields [49][50][51][52][53]. However, none of these papers combine the AROMAN method with fuzzy Z-numbers, which is the methodology of this paper.…”

“…This method combines the normalized data from the two-stage normalization and generates an average matrix from it [49,50]. The classical AROMAN method has been improved by extending it with ordinary fuzzy numbers to improve its applicability [51][52][53]. However, by integrating fuzzy sets into the model, it is not possible to reflect the hesitations of decisionmakers when making their judgments.…”