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

Kucuksari, Sadik; Pamučar, Dragan; Deveci, Muhammet; Erdoğan, Nuh; Delen, Dursun · 2023 · OpenAlex-citations

DOI: 10.1016/j.ins.2023.119443

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Summary

This study addresses the challenge of identifying and prioritizing the factors that influence consumer adoption of electric vehicles (EVs). While EVs offer environmental and socioeconomic benefits, widespread adoption is hindered by barriers such as high capital costs, range anxiety, and inadequate charging infrastructure. Existing literature often relies on surveys of potential users who have not experienced driving an EV, leading to biased perceptions, and lacks a comprehensive ranking of the relative importance of various purchase criteria. To address this, the authors propose a novel multi-criteria decision-making (MCDM) model based on a rough extension of the Ordinal Priority Approach (OPA). This method aims to provide a more objective and precise calculation of criterion weights by handling the uncertainty and inaccuracies inherent in survey data, while also accommodating decision-makers' risk attitudes through nonlinear aggregation functions. The methodology utilizes data from a large-scale post-EV test drive survey conducted during the Smart Columbus Ride & Drive Roadshow in 2017. Seventy-seven participants rated twelve decision-making criteria on a five-point Likert scale, covering economic, vehicle characteristic, and environmental attributes. The proposed Rough OPA model transforms these linguistic assessments into an aggregated rough linguistic matrix using Bonferroni functions. This approach defines lower and upper approximation limits for each criterion, allowing for the consideration of interrelationships between criteria and flexible representation of uncertainty. The model then ranks the criteria and calculates rough weight coefficients by solving a multi-objective nonlinear mathematical model, which is subsequently transformed into a linear model for solution. This process avoids the consistency issues and scale limitations associated with traditional pairwise comparison methods like AHP or BWM, particularly when dealing with a large number of criteria. The results reveal that vehicle-related characteristics are significantly more important to EV buyers than economic or environmental attributes. Specifically, "Reliability" was identified as the most significant criterion, followed closely by "Quality of Workmanship" and "Safety of a Vehicle." In contrast, "Daily Commute Distance" was found to be the least significant factor. The study also notes that while economic factors like price and federal incentives are considered, they rank lower than technical vehicle features. Sensitivity analysis confirmed the robustness of the model, demonstrating that marginal changes in parameters did not alter the final ranking order of the criteria. The significance of this research lies in its contribution to a more accurate and objective decision support system for EV adoption. By utilizing actual post-test drive data and a robust MCDM framework, the study provides policymakers and manufacturers with clear insights into user priorities, emphasizing that technical reliability and vehicle performance are primary drivers for adoption. The proposed Rough OPA model offers a scalable and consistent method for handling uncertain expert judgments in complex decision-making scenarios, enhancing the understanding of consumer preferences beyond what traditional survey analysis can provide.

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