Johnn Syu-Ning, Nikkhah Hasan, Tsai Meng-Lin, Avraamidou Styliani, Beykal Burcu, Charitopoulos Vassilis M
University College London, Department of Chemical Engineering, The Sargent Centre for Process Systems Engineering, London, WC1E 7JE, UK.
University of Connecticut, Department of Chemical & Biomolecular Engineering, Storrs, CT, USA.
Syst Control Trans. 2025;4:117-122. doi: 10.69997/sct.169891. Epub 2025 Jun 27.
Planning and scheduling are integral components of process supply chains. The presence of data correlation, particularly multivariate demand data dependency, can pose significant challenges to the decision-making process. This necessitates the consideration of dependency structures inherent in the underlying data to generate good-quality, feasible solutions to optimisation problems such as planning and scheduling. This work proposes a chance-constrained optimisation framework integrated with copulas, a non-parametric data estimation technique to forecast uncertain demand levels in accordance with specified risk thresholds in the context of a planning and scheduling problem. We focus on the integrated planning and scheduling problem following a bi-level optimisation formulation. The estimated demand forecasts are subsequently utilised within the Data-driven Optimisation of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework to solve the integrated optimisation problem, and derive decisions with guaranteed demand satisfaction rates. Computational experiments demonstrate that our proposed copula-based chance-constrained optimisation framework can incorporate demand correlation and achieve higher joint demand satisfaction rate, lower total costs with higher efficiency.
规划和调度是流程供应链的重要组成部分。数据相关性的存在，特别是多变量需求数据的依赖性，会给决策过程带来重大挑战。这就需要考虑基础数据中固有的依赖结构，以便为规划和调度等优化问题生成高质量、可行的解决方案。这项工作提出了一个结合了连接函数的机会约束优化框架，连接函数是一种非参数数据估计技术，用于在规划和调度问题的背景下，根据指定的风险阈值预测不确定的需求水平。我们专注于采用双层优化公式的综合规划和调度问题。随后，在双层混合整数非线性问题的数据驱动优化（DOMINO）框架内利用估计的需求预测来解决综合优化问题，并得出具有保证需求满足率的决策。计算实验表明，我们提出的基于连接函数的机会约束优化框架能够纳入需求相关性，并以更高的效率实现更高的联合需求满足率和更低的总成本。
