AI 中文总结
研究供应链网络中系统需求不确定性和风险传播，利用随机网络属性建模，验证牛鞭效应是固有拓扑属性，扩展到瞬态环境，纳入相关机制，通过数据驱动实验展示局部中断引发的影响。
AI 中文摘要
本研究提供了一个定量框架，用于分析一般供应链网络中的系统需求不确定性和风险传播级联。通过利用报童范式中嵌入的随机网络的属性，我们对均衡和瞬态运行状态下的多级网络进行建模。我们通过数学验证，通常称为牛鞭效应的系统波动行为完全作为协调物流网络不可避免的固有拓扑属性存在，与传统运行噪声或信息可见性约束无关。将此范式扩展到瞬态环境，我们将库存消耗期建模为多维斯科罗霍德反射问题。至关重要的是，我们通过纳入非线性价格弹性机制和动态贸易关系重新平衡来内生市场清算反馈回路，展示了分散的理性行动如何与物理容量瓶颈共同演化以加速系统网络退化。最后，我们通过映射全球石油贸易动态的数据驱动数值实验来实施该框架，展示了局部瓶颈中断，如霍尔木兹海峡的容量冲击，如何随着时间的推移引发非线性级联缺货和主权缓冲之间的系统重新分配。
英文摘要
This study provides a quantitative framework for analysis of systemic demand uncertainty and risk propagation cascades across general supply chain networks. By leveraging properties derived from stochastic networks embedded within a Newsvendor paradigm, we model multi-echelon networks under equilibrium and transient operational regimes. We mathematically validate that the systemic volatility behavior commonly referred to as the Bullwhip effect persists entirely as an unavoidable, inherent topological property of coordinated logistics networks, independent of traditional operational noise or information visibility constraints. Extending this paradigm to transient environments, we model inventory drawdown horizons as a multi-dimensional Skorokhod reflection problem. Crucially, we endogenize market-clearing feedback loops by incorporating non-linear price elasticity mechanisms and dynamic trade relation rebalancing, demonstrating how decentralized rational actions co-evolve with physical capacity bottlenecks and can accelerate systemic network degradation. Finally, we operationalize the framework through a data-driven numerical experiment mapping global oil trade dynamics, showing how localized chokepoint disruptions, such as a capacity shock in the Strait of Hormuz, trigger non-linear cascading stockouts and systemic reallocation across sovereign buffers over time.
CommentsWorking paper
