Coping with uncertainty in system parameters is a prominent hurdle when scheduling multi‐purpose batch plants. In this context, our previously introduced multi‐stage adjustable robust optimization (ARO) framework has been shown to obtain more profitable solutions, while maintaining the same level of immunity against risk, as compared to traditional robust optimization approaches. This paper investigates the amenability of existing deterministic continuous‐time scheduling models to serve as the basis of this ARO framework. A comprehensive computational study is conducted that compares the numerical tractability of various models across a suite of literature benchmark instances and a wide range of uncertainty sets. This study also provides, for the first time in the open literature, robust optimal solutions to process scheduling instances that involve uncertainty in production yields. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3055–3070, 2018

中文翻译：

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可调鲁棒优化的连续时间过程调度模型的理论和计算比较

调度多功能批处理工厂时，应对系统参数的不确定性是一个突出的障碍。在这种情况下，与传统的鲁棒优化方法相比，我们先前介绍的多阶段可调整鲁棒优化（ARO）框架已显示出可以获得更多有利可图的解决方案，同时保持了相同的抗风险能力。本文研究了现有确定性连续时间调度模型是否适合作为此ARO框架的基础。进行了全面的计算研究，比较了一系列文献基准实例和各种不确定性集之间各种模型的数值可处理性。这项研究还首次在公开文献中提供了 针对涉及产量不确定性的过程调度实例的强大的最佳解决方案。©2018美国化学工程师学会AIChE J，64：3055–3070，2018年