面向供应链网络优化的生成式概率规划
Generative Probabilistic Planning for Optimizing Supply Chain Networks
AI总结：
针对传统供应链规划方法易局部最优、可扩展性差的问题，提出结合注意力GNN、离线深度强化学习与策略仿真的生成式概率规划GPP，可实现全局优化的动态供应规划，经企业数据验证能显著提升性能与盈利。
摘要：
企业中的供应链网络通常由包含各类节点和边的复杂拓扑图构成，承载着大量供需波动显著的产品。然而，随着供应链网络规模和复杂度的提升，传统供应链规划方法（例如基于启发式规则和运筹学的系统中的方法）往往易陷入局部最优，或缺乏计算可扩展性，导致网络中各节点的供需出现严重失衡。本文提出一种新型生成式AI技术，我们称之为生成式概率规划（Generative Probabilistic Planning, GPP）。GPP可生成在时间跨度内跨所有网络节点全局优化的动态供应行动计划，以适配利润最大化或服务水平最大化等不断变化的目标，同时将时变的概率性需求、交付周期和生产条件纳入考量。GPP利用基于注意力的图神经网络（GNN）、离线深度强化学习（Offline RL）和策略仿真来训练生成式策略模型，并通过概率仿真生成最优规划，可有效应对各类不确定性。我们使用一家拥有复杂供应链网络的全球消费品公司的历史数据开展实验，结果表明GPP可实现供应链网络的目标自适应、概率鲁棒性动态规划，为企业带来性能和盈利能力的显著提升。我们的工作对塑造AI在供应链领域的应用轨迹具有关键作用。
英文摘要：
Supply chain networks in enterprises are typically composed of complex topological graphs involving various types of nodes and edges, accommodating numerous products with considerable demand and supply variability. However, as supply chain networks expand in size and complexity, traditional supply chain planning methods (e.g., those found in heuristic rule-based and operations research-based systems) tend to become locally optimal or lack computational scalability, resulting in substantial imbalances between supply and demand across nodes in the network. This paper introduces a novel Generative AI technique, which we call Generative Probabilistic Planning (GPP). GPP generates dynamic supply action plans that are globally optimized across all network nodes over the time horizon for changing objectives like maximizing profits or service levels, factoring in time-varying probabilistic demand, lead time, and production conditions. GPP leverages attention-based graph neural networks (GNN), offline deep reinforcement learning (Offline RL), and policy simulations to train generative policy models and create optimal plans through probabilistic simulations, effectively accounting for various uncertainties. Our experiments using historical data from a global consumer goods company with complex supply chain networks demonstrate that GPP accomplishes objective-adaptable, probabilistically resilient, and dynamic planning for supply chain networks, leading to significant improvements in performance and profitability for enterprises. Our work plays a pivotal role in shaping the trajectory of AI adoption within the supply chain domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
