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Collaborative Research: Operations-Driven Machine Learning

协作研究：操作驱动的机器学习

基本信息

批准号：
1762744
负责人：
Paul Grigas
金额：
$ 29.01万
依托单位：
University of California-Berkeley
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1762744&HistoricalAwards=false
关键词：
Collaborative Research Operations Driven Machine

项目摘要

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.

该奖项将通过利用数据可用性和可获得性的提高来改进业务决策，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无处不在，即使是运营的渐进式改进也会对运输、物流、医疗保健交付、供应链管理等行业的竞争力产生重大影响。同样，公共部门的服务运营涉及明智地投资有限的公共资源的决策。正在进行的数据革命为利用大规模数据改进运营决策创造了巨大的机遇。该奖项将支持对新技术的研究，以便在运营管理中有效利用这些数据。该项目为解决业务决策提供了一个广泛适用的框架，并将在实践中提高绩效和效率。该项目将涉及与不同组织的外展活动，包括一个非营利性寄养机构。当前的运营决策往往涉及两个重大挑战：预测和优化。这些任务通常是按顺序处理的：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。该项目提出了一个新的、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，运营绩效是通过预测参数生成的解决方案的真实客观值来衡量的。这个项目研究了SPO框架中新的损失函数的统计和计算性质，包括凸替代和非凸公式。该项目还将开发用于训练机器学习模型的新算法，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、健壮性、不同的数据基元和具有勘探-开采权衡的动态数据收集。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。