Distributionally robust optimization (DRO) is an emerging and effective method to address the inexactness of probability distributions of uncertain parameters in decision-making under uncertainty. We propose an effective DRO framework for planning and scheduling under demand uncertainties. A novel data-driven approach is proposed to construct ambiguity sets based on principal component analysis and first-order deviation functions, which help excavating accurate and useful information from uncertainty data. Moreover, it leads to mixed-integer linear reformulations of planning and scheduling problems. To account for the multi-stage sequential decision-making structure in process operations, we further develop multi-stage DRO models and adopt affine decision rules to address the computational issue. Applications in industrial-scale process network planning and batch process scheduling demonstrate that, the proposed DRO approach can effectively leverage uncertainty data information, better hedge against distributional ambiguity, and yield more profits.

分布鲁棒优化（DRO）是一种新兴的有效方法，用于解决不确定性条件下决策中不确定性参数的概率分布的不精确性。我们提出了一个有效的DRO框架，用于在需求不确定性下进行计划和调度。提出了一种新的数据驱动方法，该方法基于主成分分析和一阶偏差函数构造歧义集，这有助于从不确定性数据中挖掘出准确而有用的信息。此外，它导致了规划和调度问题的混合整数线性重新编制。为了解决流程操作中的多阶段顺序决策结构，我们进一步开发了多阶段DRO模型，并采用仿射决策规则来解决计算问题。