在物理互联网中加强供应链管理:混合SAGA方法
Weiqi Yan1, Nan Li2, Xin Zhang3
1Xi'an University of Finance and Economics, Xi'an, 710049, China. 1743231808@qq.com.
Scientific reports
|December 5, 2023
概括
这项研究介绍了一种混合模拟化-遗传算法 (SA-GA) 用于先进的物理互联网 (PI) 库存补充. 这种新的方法优化了动态需求和多目标因素,以实现有效的PI物流管理.
科学领域:
- 运营研究 运营研究
- 供应链管理 供应链管理
- 计算机科学 计算机科学
背景情况:
- 物理互联网 (PI) 由于其动态和复杂的性质,为库存管理带来了独特的挑战.
- 现有的库存补充策略往往难以适应PI环境中的不断变化的需求和多目标要求.
- 需要先进的优化技术来提高PI物流的效率和响应性.
研究的目的:
- 专门为物理互联网 (PI) 开发和介绍一种先进的库存补充优化方法.
- 引入一种新的混合模拟化-遗传算法 (SA-GA),用于在IP库存管理中平衡勘探和开发.
- 将动态需求和多目标优化整合到PI物流的强大的数学框架中.
主要方法:
- 开发一种混合模拟化-遗传算法 (SA-GA) 用于库存优化.
- 制定一个丰富的数学模型,包括动态需求和多目标考虑.
- 将实时数据分析集成到动态库存补充战略中.
主要成果:
- 拟议的SA-GA算法证明了PI库存管理的计算效率和适应性.
- 混合方法有效平衡勘探和开采,从而使库存的补充得到最佳化.
- 增强的数学模型为理论分析和PI环境中的实际应用提供了多功能工具.
结论:
- SA-GA算法代表了物理互联网库存管理的重大进步.
- 集成动态数据分析和强大的数学模型提高了PI物流的适应性和响应性.
- 这项研究弥合了IP库存管理现有的差距,并为未来的研究和应用提供了宝贵的参考.
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