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在供应链管理中使用复杂的毕达哥拉斯模糊软集进行仓库选址的算法解决方案
Abaker A Hassaballa1,2, Ali Asghar3, Tasadduq Niaz4
1Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia.
Scientific reports
|August 29, 2025
概括
这项研究引入了一种使用复杂皮塔哥拉斯模糊软集 (CPFSS) 的新算法,以改进仓库选址. 该方法有效地处理数据不确定性,以更好地管理供应链和更快的交付.
科学领域:
- 运营研究
- 供应链管理
- 决策科学
背景情况:
- 全球贸易依赖于高效的供应链管理和战略分销商.
- 越来越多的客户需求需要快速可靠的配送服务.
- 对于企业,特别是电子商务来说,战略性仓库选址至关重要,以满足交货期望.
研究的目的:
- 开发一种新的分布中心选址算法.
- 为了解决与选址过程有关的数据不确定性和模糊性.
- 通过知情决策提高供应链运营的效率和可靠性.
主要方法:
- 在复杂的毕达哥拉斯模糊软集 (CPFSS) 框架内制定距离测量.
- 基于这些CPFSS距离测量的新算法的开发.
- 将算法应用于选择仓库地点的现实案例研究.
主要成果:
- 拟议的基于CPFSS的算法有效处理选址数据中的不确定性.
- 该算法成功应用于HCRFT手工艺品仓库的案例研究.
- 通过与现有模型的比较验证了算法的有效性.
结论:
- 新的CPFSS方法为复杂的决策问题提供了一个强大的框架,比如仓库选址.
- 开发的算法为优化供应链物流提供了实用和有效的工具.
- 这项研究强调了先进的数学工具在应对现实世界的商业挑战方面的重要性.
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