AI 中文总结
本文提出一种数据驱动的供应链分析方法，整合统计学习与鲁棒优化，涵盖需求预测、库存控制及分布鲁棒优化，为设计韧性自动化系统提供理论基础和算法指南。
AI 中文摘要
现代供应链网络日益依赖实时数据来应对结构性不确定性、市场波动和运营中断。本手稿弥合了统计数据驱动学习与物流和运营管理中稳健决策框架之间的差距。我们提出了对供应链分析的全面、数学上严谨的处理，从经验需求预测过渡到不确定性下的最优库存和网络控制。探讨的关键主题包括样本最小化、时变库存补货的动态规划递归、网络履行框架，以及通过传输理论进行先进分布鲁棒优化（DRO）以对冲罕见事件。通过将预测性统计模型与规范性控制算法（如用于车辆路径的列生成和非齐次排队机制）相结合，本文为设计具有韧性、数据驱动的自动化系统提供了必要的基础工具。它既作为理论蓝图，也作为算法指南，服务于机器学习、数学优化和应用概率交叉领域的研究人员和从业者。
英文摘要
Modern supply chain networks increasingly rely on real-time data to navigate structural uncertainties, market volatility, and operational disruptions. This manuscript bridges the gap between statistical data-driven learning and robust decision-making frameworks in logistics and operations management. We present a comprehensive, mathematically rigorous treatment of supply chain analytics, moving from empirical demand forecasting to optimal inventory and network control under uncertainty. Key topics explored include sample minimization, dynamic programming recursions for time-varying inventory replenishment, network fulfillment frameworks, and advanced distributionally robust optimization (DRO) via transport theory to hedge against rare events. By integrating predictive statistical models with prescriptive control algorithms, such as column generation for vehicle routing and non-homogeneous queueing regimes, this text provides the foundational tools necessary for designing resilient, data-driven automated systems. It serves as both a theoretical blueprint and an algorithmic guide for researchers and practitioners operating at the intersection of machine learning, mathematical optimization, and applied probability.
CommentsDraft chapters / working book manuscript, 101 pages
