Ziemba Paweł
Faculty of Economics, Finance and Management, University of Szczecin, Cukrowa 8, Szczecin 71-004, Poland.
Data Brief. 2021 May 8;36:107118. doi: 10.1016/j.dib.2021.107118. eCollection 2021 Jun.
The data presented in this article describes a multi-criteria decision problem, where 13 criteria and 14 alternatives have been taken into account, consisting in the selection of an electric vehicle. The data set contains: (1) the parameters of the electric vehicles concerned included in the alternative performance model, (2) the weights of the criteria for assessing the vehicles, preference functions and thresholds constituting the preference model, (3) the overall performances and rankings of the alternatives (electric vehicles concerned). The data on vehicle parameters were collected from reports, catalogues and websites of car manufacturers and then processed into a decision table. In turn, data constituting various random preference models were generated using the Monte Carlo method. The overall performances and ranks of the alternatives were obtained using the MCDA (multi-criteria decision aid) method called NEAT F-PROMETHEE (New Easy Approach To Fuzzy Preference Ranking Organization METHod for Enrichment Evaluation), based on the performance model (decision table) and individual preference models. By linking vehicle parameters, preference models and vehicle rankings, the data allow, among other things, determining the impact of the preference model (weights of criteria, preference functions, thresholds) on the obtained vehicle rankings. The data also allow determining the probability of individual vehicles taking a specific position in the ranking on the basis of vehicle parameters, and regardless of the preferences of decision makers. Therefore, the data presented are valuable for practitioners and theorists dealing with electric vehicles and management, and in particular decision support. In the context of decision support, this data is also valuable to consumers considering the purchase of an electric vehicle, electric vehicle manufacturers, and dealers because it indicates the vehicles with the greatest market potential and user acceptance. This fact was confirmed by the research article entitled "Multi-criteria approach to stochastic and fuzzy uncertainty in the selection of electric vehicles with high social acceptance" [1] linked to this data article.
本文所呈现的数据描述了一个多标准决策问题，其中考虑了13个标准和14个备选方案，即选择一款电动汽车。数据集包含：(1) 备选性能模型中所涉及电动汽车的参数；(2) 用于评估车辆的标准权重、构成偏好模型的偏好函数和阈值；(3) 备选方案（所涉及电动汽车）的整体性能和排名。车辆参数数据是从汽车制造商的报告、产品目录和网站收集而来，然后处理成决策表。反过来，构成各种随机偏好模型的数据是使用蒙特卡罗方法生成的。基于性能模型（决策表）和个体偏好模型，使用名为NEAT F-PROMETHEE（用于丰富评估的模糊偏好排序组织方法的新简易方法）的多标准决策辅助（MCDA）方法获得备选方案的整体性能和排名。通过将车辆参数、偏好模型和车辆排名相联系，这些数据尤其能够确定偏好模型（标准权重、偏好函数、阈值）对所获车辆排名的影响。这些数据还能根据车辆参数，而不考虑决策者的偏好，确定个别车辆在排名中处于特定位置的概率。因此，所呈现的数据对于处理电动汽车和管理问题，特别是决策支持的从业者和理论家来说是有价值的。在决策支持的背景下，这些数据对于考虑购买电动汽车的消费者、电动汽车制造商和经销商也很有价值，因为它指出了具有最大市场潜力和用户接受度的车辆。与本文数据相关的研究文章《具有高社会接受度的电动汽车选择中的随机和模糊不确定性的多标准方法》[1]证实了这一事实。
