Beykal Burcu, Avraamidou Styliani, Pistikopoulos Efstratios N
Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USA.
Center for Clean Energy Engineering, University of Connecticut, Storrs, CT 06269, USA.
Comput Chem Eng. 2022 Jan;156. doi: 10.1016/j.compchemeng.2021.107551. Epub 2021 Sep 28.
The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. In this paper, we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints.
供应链不同层次间相互关联要素的协调对于所有工业流程至关重要，也是实现最优决策的关键。然而，对这种相互依存系统的建模和优化仍然很繁琐。在本文中，我们使用混合整数双层多跟随者规划和数据驱动优化方法，解决需求不确定情况下中期规划和短期调度问题的同步建模与优化。双层多跟随者规划从整体上对供应链管理不同层次间的自然层级进行建模，而情景分析和数据驱动优化使我们能够在各种需求考量下获取综合公式的保证可行解。我们使用DOMINO框架解决这类具有挑战性问题的数据驱动优化，该框架最初是为解决单领导者单跟随者双层优化问题以保证可行性而开发的。此框架被扩展以解决单领导者多跟随者随机公式，其性能通过著名的单产品和多产品流程调度案例研究进行表征。通过我们的数据驱动算法方法，我们为同时规划和调度问题的线性和非线性混合整数双层公式提供保证可行解，并进一步表征调度层级复杂性对解性能的影响，该问题涉及数百个连续和二元变量以及数千个约束。
