AI-enabled Automated Algorithm Selection and Configuration for Mathematical Optimization Problems

数学优化是化工决策的基石。在诸如与可再生能源相互作用的化学过程系统的设计和实时操作、弹性供应链网络的设计以及化学生产设施的可持续操作等问题中，底层过程的复杂行为以及多个时间和空间尺度的存在导致大规模和复杂的优化配方。用于解决这些问题的算法已经并且继续被开发，但是（i）它们的实现是具有挑战性的并且计算密集的，因为它们涉及许多步骤，并且（ii）不清楚先验哪种算法更适合于给定的问题。在这项研究计划中，将采用最先进的人工智能（AI）和机器学习（ML）工具来选择和实现给定问题的最佳优化算法。这些方法将自动化并纳入开放源码软件，以促进行业从业人员和学术研究人员解决复杂问题。在这项研究计划中，研究生将接受跨化学工程，数学优化和数据科学的基础研究培训。在明尼阿波利斯，明尼苏达州农村地区的高中以及明尼苏达州的美国印第安人，苗族和索马里社区的外联活动将突出数据科学在化学工业中日益重要的地位，并旨在激励化学工程的职业生涯。这项研究将利用人工智能（AI）和机器学习（ML）中最先进的方法，实现自动选择和配置最先进的优化算法，以解决过程系统工程中出现的非线性和混合整数非线性问题。该研究将解决以下任务：（i）将开发一个图形神经网络框架，以捕获有关变量和约束的详细信息的形式来表示一般非线性优化问题;（ii）将采用几何深度学习方法来选择最佳解决方案策略并以自动方式调整优化算法;（iii）采用可解释的人工智能方法，以解释最优化问题与最优化解算器的计算性能之间的关系。将在开放源代码软件中开发和实施的优化框架将有助于过程系统研究人员通过选择最合适的解决方法和最佳调整的解决算法来有效解决复杂决策问题。此外，该框架将使当前建模实践和/或优化算法中可能存在的性能瓶颈检测成为可能，并反过来指导问题重构或算法改进。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Mathematical optimization is the cornerstone of decision-making in chemical engineering. In problems such as the design and real-time operation of chemical process systems interacting with renewable energy resources, the design of resilient supply chain networks, and the sustainable operation of chemical production facilities, the complex behavior of the underlying processes and the presence of multiple temporal and spatial scales lead to large-scale and complex optimization formulations. Algorithms for solving these problems have been and continue to be developed, but (i) their implementation is challenging and computationally intensive since they involve numerous steps, and (ii) it is not clear a-priori which algorithm is better suited for a given problem. In this research program, state-of-the-art artificial intelligence (AI) and machine learning (ML) tools will be employed to select and implement the best optimization algorithm for a given problem. These methods will be automated and incorporated in open-source software to facilitate the solution of complex problems by industry practitioners and academic researchers alike. In this research program, graduate students will be trained in fundamental research cutting across chemical engineering, mathematical optimization, and data science. Outreach activities to high schools in Minneapolis, rural Minnesota, and the American Indian, Hmong, and Somali communities in Minnesota will highlight the increasing importance of data science in the chemical industry and will aim to motivate careers in chemical engineering. This research will leverage state-of-the-art methods in artificial intelligence (AI) and machine learning (ML) to enable the automated selection and configuration of state-of-the-art optimization algorithms for the solution of nonlinear and mixed integer nonlinear problems that arise in process systems engineering. The research will address the following tasks: (i) a graph neural network framework will be developed to represent generic nonlinear optimization problems in a form that captures detailed information on the variables and constraints; (ii) geometric deep learning methods will be employed for the selection of the best solution strategy and the tuning of optimization algorithms in an automated manner; (iii) explainable AI methods will be employed to decode the relationship between optimization problems and the computational performance of optimization solvers. The optimization framework to be developed and implemented in open-source software will facilitate process systems researchers’ ability to solve complex decision-making problems efficiently by selecting the most appropriate solution method and optimally tuned solution algorithm. In addition, this framework will make possible detection of possible performance bottlenecks in current modeling practices and/or optimization algorithms, and in turn guide problem reformulation or algorithm improvements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.