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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:BROWN UNIVERSITY IN PROVIDENCE IN THE STATE OF RHODE ISLAND AND PROVIDENCE PLANTATIONS
Doing Business As Name:Brown University
PD/PI:
  • Philip N Klein
  • (401) 863-7680
  • klein@brown.edu
Award Date:12/04/2017
Estimated Total Award Amount: $ 50,000
Funds Obligated to Date: $ 50,000
  • FY 2018=$50,000
Start Date:01/01/2018
End Date:06/30/2018
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:I-Corps: Optimization Algorithms for Mapping
Federal Award ID Number:1801106
DUNS ID:001785542
Parent DUNS ID:001785542
Program:I-Corps
Program Officer:
  • Steven Konsek
  • (703) 292-7021
  • skonsek@nsf.gov

Awardee Location

Street:BOX 1929
City:Providence
State:RI
ZIP:02912-9002
County:Providence
Country:US
Awardee Cong. District:01

Primary Place of Performance

Organization Name:Brown University, Computer Science Dept.
Street:115 Waterman Street
City:Providence
State:RI
ZIP:02912-9002
County:Providence
Country:US
Cong. District:01

Abstract at Time of Award

The broader impact/commercial potential of this I-Corps project is to provide organizations with easy access to efficient logistical planning. The project will potentially assist emerging businesses and other organizations by providing optimization services in road maps such as finding delivery routes, selecting locations for new facilities, and selecting routes for cables or pipes. This allows organizations facing such problems to focus on their core mission and outsource the optimization. This project will enable such organizations to provide better service at lower cost. This I-Corps project draws on substantial research aimed at developing of efficient algorithms for finding optimal and near-optimal solutions to optimization problems in planar networks, networks that can be drawn on a flat surface. Since road maps are mostly planar, these algorithms can be adapted to work on road maps. The project builds on an implementation of the underlying algorithmic methods. The implementation can very quickly find high-quality solutions even for very large networks. The project will explore the commercialization of algorithmic techniques in real-world contexts, and suggest new optimization problems requiring further research.

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