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Updated: Jul 25, 2025
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A Quantitative Fitness Analysis Workflow
Published on: August 13, 2012
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基于多个标准的电动汽车比较使用 q-Rung 整形pair 模糊数字
Sanjib Biswas1, Aparajita Sanyal2, Darko Božanić3
1Decision Science & Operations Management Area, Calcutta Business School, Diamond Harbour Road, Bishnupur Kolkata 743503, West Bengal, India.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本研究介绍了一种混合决策模型,用于评估印度的电动汽车 (EV). 该模型结合了和q-rung orthopair fuzzy (qROF) 方法,将电能消耗确定为关键,并将EV A7排名为最高.
科学领域:
- 可持续的运输可持续的运输
- 决策科学 决策科学 决策科学
- 汽车工程 汽车工程
背景情况:
- 评估电动汽车 (EV) 需要强大的决策模型来处理不确定性和不准确的数据.
- 现有的方法可能无法完全整合客观的技术属性与主观的用户意见,以进行全面的EV评估.
- 可持续运输计划需要有效的工具来根据多种标准选择最佳的电动汽车.
研究的目的:
- 开发和应用一种新的混合多标准决策 (MCDM) 模型来评估和排名印度的电动汽车.
- 将方法与q-rung orthopair fuzzy (qROF) 信息和爱因斯坦聚合进行整合,用于在不确定性下做出决策.
- 根据技术属性和用户意见,比较领先的电动汽车,提供一个强大的评估框架.
主要方法:
- 使用两步规范化和完全一致性检查的法来确定标准权重.
- 用 q-rung orthopair fuzzy (qROF) 信息和爱因斯坦聚合来处理不准确的数据扩展了法.
- 采用了具有两步正常化 (AROMAN) 的替代排名顺序方法来排名印度20款领先的电动汽车.
主要成果:
- 电力消耗被确定为最重要的标准,获得最高权重 (w = 0.0944).
- 根据提出的决策模型,电动汽车A7被评为最佳选择.
- 该模型通过敏感性分析和与其他MCDM方法的比较,证明了它的稳定性和稳定性.
结论:
- 新的混合MCDM模型有效地整合了客观和主观信息,用于在不确定的环境中进行EV评估.
- 这些发现为消费者和决策者在选择印度市场最佳电动汽车方面提供了宝贵的见解.
- 这项研究为可持续运输和其他复杂的评估问题提供了强大的决策框架.
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