A new rough ordinal priority-based decision support system for purchasing electric vehicles

Kucuksari, S., Pamucar, D., Deveci, M., Erdogan, N. ORCID: 0000-0003-1621-2748 and Delen, D., 2023. A new rough ordinal priority-based decision support system for purchasing electric vehicles. Information Sciences, 647: 119443. ISSN 0020-0255

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Abstract

This study proposes a novel multi-criteria decision-making (MCDM) model based on a rough extension of the Ordinal Priority Approach (OPA) to determine the order of importance of users' perspectives on Electric Vehicle (EV) purchases. Unlike conventional methods that rely on predefined ranks for criteria weighting coefficients, the proposed rough OPA method employs an aggregated rough linguistic matrix, enabling a more precise and unbiased calculation of interval values. Moreover, the model addresses inherent uncertainties by incorporating nonlinear aggregation functions, accommodating decision makers' risk attitudes for flexible decision-making. To validate the model's efficacy, a large-scale post-EV test drive survey is conducted, enabling the determination of relative criterion importance. Sensitivity analysis confirms the robustness of the model, demonstrating that marginal changes in parameters do not alter the ranking order. The results unveil the significance of the reliability criterion and reveal that vehicle-related characteristics outweigh economic and environmental attributes in the decision-making process. Overall, this innovative MCDM model contributes to a more accurate and objective analysis, enhancing the understanding of users' preferences and supporting informed decision-making in EV purchases.

Item Type:	Journal article
Publication Title:	Information Sciences
Creators:	Kucuksari, S., Pamucar, D., Deveci, M., Erdogan, N. and Delen, D.
Publisher:	Elsevier BV
Date:	November 2023
Volume:	647
ISSN:	0020-0255
Identifiers:	Number Type 10.1016/j.ins.2023.119443 DOI 1790536 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	22 Feb 2024 09:38
Last Modified:	22 Feb 2024 09:38
URI:	https://irep.ntu.ac.uk/id/eprint/50914

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