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EAGER: Computational Operations Research Exchange (CORE)

EAGER: Computational Operations Research Exchange (CORE)
EAGER:计算运筹研究交流中心(CORE)
批准号:
1822327
负责人:
Suvrajeet Sen
金额:
$29.7万
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
项目状态:
已结题
起止时间:
2018-04-01 至 2021-03-31

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中文摘要
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英文摘要
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.
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会议论文
DOI: 10.1287/ijoc.2019.0929
发表时间: 2021-12-01
期刊: INFORMS JOURNAL ON COMPUTING
影响因子: 2.1
作者: [Gangammanavar, Harsha, Liu, Yifan, Sen, Suvrajeet]
通讯作者: Sen, Suvrajeet
EAGER: Renewables: Collaborative Proposal on Stochastic Unit Commitment with Topology Control Recourse for Networks with High Penetration of Distributed Renewable Resources
  • 批准号:
    1548847
  • 项目类别:
    Standard Grant
  • 资助金额:
    $15.0万
  • 财政年份:
    2015
  • 负责人:
    Suvrajeet Sen
  • 依托单位:
A Task Force to Study Operations Research as a Catalyst for Engineering Grand Challenges
  • 批准号:
    1243182
  • 项目类别:
    Standard Grant
  • 资助金额:
    $2.5万
  • 财政年份:
    2012
  • 负责人:
    Suvrajeet Sen
  • 依托单位:
Collaborative Research: Stochastic Multi-scale Optimization for Energy Resource Planning
Workshop for Cyber-enabled Discovery and Innovation in Operations Research; Seattle, Washington; November 3-7, 2007
国内基金
海外基金
Computational Methods for Analyzing Toponome Data