费尔马特模糊的语言术语集基于语言尺度函数与Dombi聚合运算符及其应用到多个标准的群组决策问题
Omar Barukab1, Asghar Khan2, Sher Afzal Khan3
1Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 411, 21911, Rabigh, Jeddah, Saudi Arabia.
Heliyon
|September 12, 2024
概括
选择工业地点是复杂的. 本研究引入了一种新的Fermatean Fuzzy Linguistic (FFL) 方法,使用Dombi聚合运算符来处理不确定的信息,改善战略地点选择的多标准决策.
科学领域:
- 运营研究 运营研究
- 决策科学 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:
- 工业地点选择是一个复杂的多标准决策 (MCDM) 问题.
- 决策者由于复杂的环境或有限的知识,经常面临模两可的信息.
- 现有的方法可能会在选择地点的标准中与固有的不确定性和语言模糊性作斗争.
研究的目的:
- 提出一个全面的框架,用于战略工业地点的选择.
- 引入一个新的费尔马特语模糊语言 (FFL) 术语集来表示不确定的评估信息.
- 开发一种新的基于FFL Dombi聚合运营商的多标准集团决策技术.
主要方法:
- 开发一个新的Fermatean Fuzzy Linguistic (FFL) 术语集.
- 建立用于FL信息的操作原则和聚合运营商 (FFLDWA,FFLDWG).
- 使用拟议的FFL Dombi运营商构建一个多标准的集团决策技术.
- 通过数值示例验证,将技术与现有方法进行比较.
主要成果:
- 拟议的FFL集实际上代表了不确定的语言评估信息.
- 开发的FFL Dombi聚合运算符提供了强大的方法来聚合模糊的语言数据.
- 新的决策技术在战略工业地点选择方面表现出灵活性和有效性.
- 数字示例证实了拟议方法的优越性和适应性.
结论:
- 费尔马特的模糊语言方法提供了一个强大的工具,用于处理工业地点选择中的模糊性.
- 整合Dombi聚合运算符提高了MCDM方法在复杂决策环境中的能力.
- 拟议的框架提供了一种可靠和灵活的方法,根据优先考虑的标准确定最佳工业地点.
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