EAGER: Computational Operations Research Exchange (CORE)

这一早期概念探索性研究补助金(AGER)奖将通过改变城市系统研究人员在研究中使用数据分析的方式来支持科学进步，促进国家繁荣和福利。由于无法获得真实世界的数据，在城市机动性、能源和水使用、公共安全和医疗保健等许多运营问题上的科学进展受到限制。目前，运筹界开发的新模型和算法通常是在模拟数据集上进行测试的，这些数据集可能不能反映真实的运行条件。该项目支持的研究将创建一个旨在克服这些限制的新型计算运筹学研究交换(CORE)平台。核心平台将允许来自不同学科的研究人员利用实际操作数据，并在现实场景中比较他们的方法和结果。该交流平台还将提供一种机制，鼓励跨学科合作，这对复杂社会问题的创新解决方案非常重要。该项目将使用系统方法来为运筹学模型建立一个可重复使用的网络基础设施。随着今天开放获取的公共数据集的提供，该平台将提供一个即插即用的范例，可以无缝地共享数据、模型和算法，使研究人员能够在真实数据集上有效地比较算法。该项目将从能源、交通和城市分析中的应用程序中提取三个用例开始。PIS将设计协议，以便使用该国许多不同地区可用的数据来实例化这些应用程序，从而允许使用区域数据集测试类似范围的模型。该平台将提供通用模型接口，其中将允许替代模型类型在通用数据集上工作，以便进行比较。PIS致力于与女性和少数族裔密切合作，并将让两名女性博士生参与这个项目。这一奖项反映了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。

This EArly-concept Grant for Exploratory Research (EAGER) award will support the progress of science and advance the national prosperity and welfare by transforming how urban systems researchers use data analytics in their studies. Scientific progress in many operational problems in urban mobility, energy and water usage, public safety, and healthcare is limited by lack of access to real-world data. Currently, new models and algorithms developed by the operations research community are often tested on simulated datasets that may not reflect real operating conditions. Research supported by this project will enable the creation of a novel Computational Operations Research Exchange (CORE) platform that aims to overcome these limitations. CORE platform will allow researchers from different disciplines to leverage actual operational data and compare their methods and results on realistic scenarios. The exchange platform will also provide a mechanism to encourage cross-disciplinary collaborations, which are important to innovative solutions to complex societal problems. The project will use a Systems-of-Systems approach to build a re-usable cyber-infrastructure for operations research models. With today's availability of open access public datasets, the platform will provide a plug-and-play paradigm which can share data, models and algorithms in a seamless manner, allowing researchers to effectively compare algorithms on real datasets. The project will begin with three use-cases drawn from applications in energy, transportation, and urban analytics. The PIs will design protocols so that these applications will be instantiated using data available across many different regions in the country, thus allowing models of similar scope to be tested using regional datasets. The platform will provide generic model interfaces, where alternative model types will be allowed to work on common datasets for comparison purposes. The PIs are committed to working closely with women andminorities and will involve two women Ph.D students in this project.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.