CRII: CIF: A Machine Learning-based Computational Framework for Large-Scale Stochastic Programming

CRII：CIF：基于机器学习的大规模随机规划计算框架

基本信息

批准号：
1948159
负责人：
Zhijie Dong
金额：
$ 17.5万
依托单位：
Texas State University - San Marcos
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948159&HistoricalAwards=false
关键词：
CRII CIF Machine Learning based

项目摘要

Modeling optimization problems under uncertainty is known as stochastic programming (SP). It has a variety of important applications, including disaster management, supply chain design, health care, and harvest planning. Most real-world problems are complicated enough to generate a very large-size SP model, which is difficult to solve. Quickly finding the optimal solutions of these models is critical for decision-making when facing uncertainties. Existing optimization algorithms have a limited capability of solving large-scale SP problems. Without being explicitly programmed, machine learning can give computers the ability to "learn" with data by using statistical techniques. The goal of this project is to create a machine learning-based computational framework to solve large-scale stochastic programming problems effectively and efficiently by integrating machine learning techniques into optimization algorithms. The project will broaden the scope and applicability of machine learning in operations research. Furthermore, this research will support the cross-disciplinary training of graduate and undergraduate students in engineering and computer sciences, as well as the development of new curricula in the interface of machine learning and optimization algorithms.The project will be the pioneering study of applying machine learning into stochastic programming, while existing works usually focus on using stochastic programming to improve the efficiency of machine learning algorithms. Motivated by the challenges from practices and limitations of current optimization algorithms, two research objectives are proposed: efficient sample generation and convergence acceleration, by taking sample average approximation and L-shaped algorithm as examples. The first research objective is to design a semi-supervised learning algorithm based on solution information to efficiently generate samples for sample average approximation. The second research objective is to develop a supervised learning algorithm to estimate a tight upper bound for expediting convergence of L-shaped method. The two research objectives will be achieved through five tasks: (1) semi-supervised learning-based scenario grouping; (2) supervised learning based representative scenario selection; (3) performance analysis for sample generation; (4) supervised learning based upper bound prediction; and (5) performance analysis for the machine learning-based L-shaped method. The successes of this project will generate a new class of theoretical optimization methods that facilitate various real-world applications in disaster management, supply chain design, health care and harvest planning.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.

在不确定性下建模优化问题称为随机编程（SP）。它具有多种重要应用，包括灾难管理，供应链设计，医疗保健和收获计划。大多数现实世界中的问题都足够复杂，可以产生非常大的SP模型，这很难解决。在面对不确定性时，很快找到这些模型的最佳解决方案对于决策至关重要。现有的优化算法能够解决大规模SP问题的能力。机器学习无明确编程，可以使计算机使用统计技术“学习”数据。该项目的目的是创建一个基于机器学习的计算框架，以通过将机器学习技术集成到优化算法中，从而有效，有效地解决大规模的随机编程问题。该项目将扩大机器学习在操作研究中的范围和适用性。此外，这项研究将支持对工程和计算机科学领域的毕业生和本科生的跨学科培训，以及在机器学习和优化算法的界面中开发新课程的开发。该项目将是将机器学习应用于随机计划的开创性研究，而现有的现有工作则集中在现有的工作中，以提高机器的效果，以提高机器的效果。由实践和当前优化算法的局限性挑战所激发的挑战，提出了两个研究目标：通过采用样品平均近似值和L形算法作为示例，提出了有效的样本产生和收敛加速度。第一个研究目标是基于解决方案信息设计半监督学习算法，以有效地生成样品以进行样品平均近似值。第二个研究目标是开发一种有监督的学习算法，以估算加快L形方法收敛的紧密上限。这两个研究目标将通过五个任务实现：（1）半监督的基于学习的方案分组；（2）基于监督学习的代表性场景选择；（3）样本生成的绩效分析；（4）基于监督学习的上限预测；（5）基于机器学习的L形方法的性能分析。该项目的成功将产生一类新的理论优化方法，以促进灾难管理，供应链设计，医疗保健和收获计划中的各种现实应用程序。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。