Development of an analysis framework for measurement-based dynamic optimization

科学的发展包括发明新思想和有效地实施新思想。一旦开发出新的工艺，优化其操作对其商业化至关重要。此外，当现有工艺面临严峻的经济和环境挑战时，优化的重要性再次显现出来。通常，优化是基于过程的数学模型进行的。然而，这些模型并不总是准确的，因此得到的数值解并不符合最优操作。因此，当前项目的上下文是基于度量的优化，从过程中获取的度量用于优化，而不是用于数学模型。在基于测量的优化技术中，在无约束静态优化领域已经有了相当大的努力，其中确定无约束的最佳工作点。然而，所提出的研究解决了更复杂和现实的约束动态优化问题，其中在运行过程中和最终产品规格存在约束的情况下寻求最佳时间分布。研究的目的是为基于测量的约束动态优化问题提供一个分析框架。这个框架将导致高效算法的发展，它不仅可以用于操作问题，如提高工业单位的生产率，而且最终也可以用于调度和供应链管理问题。

Development of science consists of inventing new ideas and implementing them efficiently. Once a new process has been developed, optimizing its operation is essential for its commercialization. Also, the importance of optimization resurfaces, when an existing process faces tough economical and environmental challenges. Usually, optimization is carried out numerically based on a mathematical model of the process. However, these models are not always accurate and the numerical solution so obtained does not correspond to the optimal operation. So, the context of the current project is measurement-based optimization, where measurements taken from the process are used for optimization, instead of a mathematical model. Among measurement-based optimization techniques, there has been considerable effort in the domain of unconstrained static optimization, where the best operating point without constraints is determined.  However, the proposed research addresses the more complex and realistic problem of constrained dynamic optimization, where optimal time profiles are sought in the presence of constraints both during the operation and on the final product specifications. The objective of the research is to provide an analytical framework for the constrained dynamic optimization problem based on measurements. This framework will lead to the development of efficient algorithms, which can be employed not only for operational problems, like improving the productivity of an industrial unit, but eventually also to scheduling and supply chain management problems.