A novel fuel supply system modelling approach for electric vehicles under Pythagorean probabilistic hesitant fuzzy sets

Various companies have developed electric vehicle (EV)-based multiple fuel supply system modeling approaches (FSSMAs). Nonetheless, no superior approach concurrently satisfies all essential criteria, including 'sustainability' and 'fuel consideration' criteria. Furthermore, benchmarking the FSSMA alternatives to determine the most sustainable ones does not come without issues. The five main most common concerns are the use of various evaluation criteria, effecting the weights of the criteria with sublayers, criteria pri-oritization, trade-offs among the criteria, and data variations. Thus, this study proposes a novel FSSMA for EV benchmarking based on two methods-the Pythagorean probabilistic hesitant fuzzy sets and fuzzy weighted zero inconsistency (PPH-FWZIC) and the measure-ment of alternatives and ranking according to the compromise solution (MARCOS)-which are integrated as a single method. The PPM-FWZIC method was developed to solve the cri-teria prioritization issue, while the MARCOS method was developed to solve the various evaluation criteria, trade-offs among the criteria, and data variation issues to benchmark the FSSMA for EV alternatives. The integrated multicriteria decision-making (MCDM) method allows the system to perform a backward scoring process (BSP) and derive a scor-ing decision matrix from the formulated decision matrices that are performed based on the feed-forward data presentation (FFDP) procedure to solve the multiple criteria layers that affect the proper assessment of the impact of a certain criterion and its subcriteria in the weighting purpose issues. Subsequently, the FSSMAs for EVs are benchmarked, and the most sustainable approach is selected. The results were tested via sensitivity analysis and the Spearman correlation coefficient. The present study is also compared with a bench-mark study based on a benchmarking checklist.(c) 2022 Elsevier Inc. All rights reserved.