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

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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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Official URL: https://doi.org/10.1016/j.ins.2023.119443

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