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Updated: Jun 28, 2025

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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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一个高效的球形模糊的MEREC-CoCoSo方法,基于新的得分函数和聚合运算符,用于集团决策
Guorou Wan1, Yuan Rong2, Harish Garg3,4,5,6
1Department of Basic Education, Sichuan College of Architectural Technology, Deyang, 618000 China.
概括
这项研究引入了一种新的决策方法,使用球形模糊集和CoCoSo技术来找到最佳太阳能发电站位置. 该方法增强了标准权重和排名,以获得最佳的地点选择.
科学领域:
- 可再生能源系统可再生能源系统
- 决策科学 决策科学
- 模糊的集合理论 模糊的集合理论
背景情况:
- 选择最佳的太阳能发电站位置是复杂的,涉及多个标准和不确定性.
- 现有的多标准组决策 (MCGDM) 方法可能无法完全解决模糊信息的细微差别.
研究的目的:
- 为最佳的太阳能发电站选址开发一个集成的MCGDM方法.
- 引入新的球形模糊集运算和一个得分函数,以改善比较.
- 建议采用球形模糊MEREC技术进行标准加权,并改进CoCoSo方法进行排名.
主要方法:
- 为球形模糊数 (SFN) 开发一种新的球形模糊得分函数.
- 定义新的操作规则和对SFN进行聚合操作.
- 应用球形模糊MEREC技术来确定标准的重要性.
- 实施了改进的球形模糊CoCoSo方法来对太阳能发电站位置进行排名.
主要成果:
- 拟议的球形模糊MEREC-CoCoSo (SF-MEREC-CoCoSo) 方法为太阳能发电站选址提供了一个强大的框架.
- 新的球形模糊分数函数和聚合运算符提高了决策准确性.
- 对比和敏感性分析证明了该方法的可行性,实用性和稳定性.
结论:
- 开发的SF-MEREC-CoCoSo方法有效地解决了最佳太阳能发电站选址的复杂性.
- 球形模糊集与CoCoSo和MEREC的集成为可再生能源规划的MCGDM提供了重大进展.
- 该研究验证了拟议的决策方法的稳定性和稳定性.
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