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

数学优化是化工决策的基石。在与可再生能源相互作用的化工过程系统的设计和实时运行、弹性供应链网络的设计以及化工生产设施的可持续运行等问题中，潜在过程的复杂行为和多重时空尺度的存在导致了大规模和复杂的优化配方。解决这些问题的算法已经并将继续发展，但是(i)它们的实现具有挑战性和计算密集型，因为它们涉及许多步骤，并且（ii）先验地不清楚哪种算法更适合给定的问题。在这个研究项目中，最先进的人工智能（AI）和机器学习（ML）工具将被用于选择和实现给定问题的最佳优化算法。这些方法将被自动化并整合到开源软件中，以方便行业从业者和学术研究人员解决复杂问题。在这个研究项目中，研究生将在化学工程、数学优化和数据科学的基础研究方面进行培训。在明尼阿波利斯的高中，明尼苏达州的农村，以及美国印第安人，苗族和索马里社区的推广活动将突出数据科学在化学工业中日益增长的重要性，并旨在激励化学工程的职业发展。本研究将利用人工智能（AI）和机器学习（ML）中最先进的方法，实现最先进优化算法的自动选择和配置，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)将开发一个图神经网络框架，以捕获有关变量和约束的详细信息的形式表示一般非线性优化问题；（ii）几何深度学习方法将被用于选择最佳解决方案策略和自动调整优化算法；（iii）可解释的人工智能方法将被用于解码优化问题与优化求解器的计算性能之间的关系。该优化框架将在开源软件中开发和实现，将有助于过程系统研究人员通过选择最合适的求解方法和最优解算法，有效地解决复杂的决策问题。此外，该框架将可能检测到当前建模实践和/或优化算法中可能存在的性能瓶颈，并反过来指导问题的重新表述或算法改进。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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.