Collaborative Research: Operations-Driven Machine Learning

该合同将通过增加数据的可用性和可访问性来改善业务决策，从而为国家的繁荣和福利做出贡献。运营决策在商业经济的各个方面都无处不在，即使是运营方面的逐步改进也会对运输、物流、医疗保健交付、供应链管理等部门的竞争力产生重大影响。同样，公共部门服务业务也涉及明智地投资有限公共资源的决策。正在进行的数据革命为利用大规模数据来改进运营决策创造了巨大的机会。该奖项将支持新技术的研究，以便在运营管理中有效利用这些数据。该项目为处理操作决策提供了一个广泛适用的框架，并将在实践中提高性能和效率。该项目将涉及与不同组织的外展活动，包括一家非营利性寄养机构。当前的运营决策通常涉及两个重大挑战：预测和优化。这些任务通常是顺序处理的：首先使用现代统计机器学习工具预测关键参数，然后在复杂的优化模型中使用这些预测做出规划决策。该项目提出了一种新的，广泛适用的框架，称为智能“预测，然后优化”（SPO），它有效地解决了预测和优化的挑战。在这个新的框架中，操作性能是由预测参数生成的解决方案的真实客观值来衡量的。该项目研究了SPO框架中新型损失函数的统计和计算特性，包括凸替代和非凸公式。该项目还将开发新的算法，用于训练机器学习模型，如线性模型、逻辑模型和决策树，使用新的损失函数，并将扩展SPO框架，以处理正则化、鲁棒性、不同的数据原语和动态数据收集，并进行探索开发权衡。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。

This award will contribute to the Nation's prosperity and welfare by capitalizing on the increased availability and accessibility of data to improve operational decision making. Operational decisions are ubiquitous in all aspects of the commercial economy, and even incremental improvements in operations can have major impacts in the competitiveness of such sectors as transportation, logistics, healthcare delivery, supply chain management. Similarly, public sector service operations involve decision making to wisely invest limited public resources. The ongoing data revolution has created great opportunities for leveraging large scale data to improve operational decision making. This award will support research in new techniques to make effective use of these data in the management of operations. This project provides a broadly applicable framework for addressing operational decisions and will result in improved performance and efficiency in practice. The project will involve outreach engagements with diverse organizations, including a nonprofit foster care agency. Current operational decision-making often involve two significant challenges: prediction and optimization. These tasks are usually addressed sequentially: key parameters are first predicted using modern statistical machine learning tools, and then planning decisions are made using these predictions within a complex optimization model. This project advances a new, broadly applicable framework, called Smart "Predict, then Optimize" (SPO), that effectively addresses the prediction and optimization challenges in tandem. In this new framework, operational performance is measured by the true objective value of the solutions generated from the predicted parameters. This project investigates the statistical and computational properties of novel loss functions in the SPO framework, including convex surrogates as well as non-convex formulations. The project will also develop new algorithms for training machine learning models, such as linear models, logistic models, and decision trees, using the new loss functions, and will extend the SPO framework to handle regularization, robustness, different data primitives, and dynamic data collection with exploration-exploitation tradeoffs.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.