用生成式模拟与迭代决策优化供应链运输，提升交付效率和利润。
Supply Chain Optimization via Generative Simulation and Iterative Decision Policies
- 通过自回归建模生成连续状态变化，减少人工规则依赖。
- 在三个真实数据集上显著提高准时交付率和利润率。
- 适合需要动态优化运输策略的物流与供应链研究者。
高响应速度和经济高效是供应链运输的关键目标，二者均受运输方式选择等战略决策影响。将高效模拟器与智能决策算法结合，可为运输策略设计提供可观测、低风险的环境。理想的仿真-决策框架需具备：(1) 跨场景泛化能力，(2) 细粒度运输动态刻画，(3) 历史经验与预测洞察融合，(4) 模拟反馈与策略迭代紧密耦合。本文提出 Sim-to-Dec 框架以满足上述需求。具体而言，该框架包含一个生成式模拟模块，采用自回归建模模拟连续状态演化，降低对领域特定规则的依赖，增强对数据波动的鲁棒性；以及一个历史-未来双感知决策模型，通过与模拟器交互进行端到端迭代优化。在三个真实世界数据集上的大量实验表明，Sim-to-Dec 显著提升了准时交付率和利润水平。
原文摘要 · Abstract (English)
High responsiveness and economic efficiency are critical objectives in supply chain transportation, both of which are influenced by strategic decisions on shipping mode. An integrated framework combining an efficient simulator with an intelligent decision-making algorithm can provide an observable, low-risk environment for transportation strategy design. An ideal simulation-decision framework must (1) generalize effectively across various settings, (2) reflect fine-grained transportation dynamics, (3) integrate historical experience with predictive insights, and (4) maintain tight integration between simulation feedback and policy refinement. We propose Sim-to-Dec framework to satisfy these requirements. Specifically, Sim-to-Dec consists of a generative simulation module, which leverages autoregressive modeling to simulate continuous state changes, reducing dependence on handcrafted domain-specific rules and enhancing robustness against data fluctuations; and a history-future dual-aware decision model, refined iteratively through end-to-end optimization with simulator interactions. Extensive experiments conducted on three real-world datasets demonstrate that Sim-to-Dec significantly improves timely delivery rates and profit.
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