EAGER: Computational Operations Research Exchange (CORE)

EARLY概念探索性研究补助金（EAGER）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，支持科学进步，促进国家繁荣和福利。在城市交通、能源和水的使用、公共安全和医疗保健等许多运营问题上的科学进步受到缺乏真实数据的限制。目前，由运筹学界开发的新模型和算法经常在可能不反映真实的操作条件的模拟数据集上进行测试。该项目支持的研究将能够创建一个新的计算运筹学交换（CORE）平台，旨在克服这些限制。CORE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个鼓励跨学科合作的机制，这对复杂社会问题的创新解决方案至关重要。该项目将使用系统的方法来建立一个可重复使用的网络基础设施的运作研究模型。 随着今天开放获取公共数据集的可用性，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，使研究人员能够有效地比较真实的数据集上的算法。 该项目开始将从能源、交通和城市分析应用中抽取三个用例。 PI将设计协议，以便使用该国许多不同地区的可用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，其中允许替代模型类型在常见数据集上工作以进行比较。 PI致力于与妇女和少数民族密切合作，并将在这个项目中涉及两名女博士生。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.