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在大型物流网络中加速平衡的MetaGradient驱动策略分解
1School of Business, Anhui Xinhua University, Hefei, Anhui, China.
PloS one
|November 19, 2025
概括
本研究引入了一个新的渐变驱动的全球物流框架,提高效率和减少环境影响. 该模型实时优化供需平衡,增强物流网络.
科学领域:
- 物流和供应链管理的物流和供应链管理.
- 运营研究 运营研究
- 计算科学 计算科学
背景情况:
- 静态模型不能充分代表动态的全球物流,导致利用不足 (例如,<70%的容量) 和不平衡 (0.57节点负载).
- 现有的算法在突然的需求变化中扎,导致了显著的预测错误 (7.8%) 和趋同延迟 (38%).
研究的目的:
- 开发一种用于实时优化大型物流网络的新框架.
- 解决静态模型的局限性,提高物流运营的效率和响应性.
主要方法:
- 一个梯度驱动的框架,集成稀疏梯度,张量分解和受约束的多目标优化.
- 实时数据处理和适应性控制机制,用于动态调整.
主要成果:
- 取得了显著的改进:成本降低了28.3%,运输时间缩短了37.3%,集装箱周转率增加了41.4%,二氧化碳排放量减少了27.7%.
- 在15个节点网络中表现出卓越的性能,容量匹配率为89.3%,根平均平方误差为0.145.
- 在基准测试中表现优于传统方法和强化学习方法.
结论:
- 拟议的框架为智能,低碳和弹性物流提供了一个可扩展的实时优化范式.
- 能够实现次秒平衡趋同和复杂物流网络中干扰后的快速恢复.
- 建立了智能和可持续的全球物流生态系统未来进步的基础.
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