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Research Spending & Results

Award Detail

Awardee:WASHINGTON UNIVERSITY, THE
Doing Business As Name:Washington University
PD/PI:
  • William Yeoh
  • (314) 747-4134
  • wyeoh@wustl.edu
Award Date:11/07/2017
Estimated Total Award Amount: $ 24,849
Funds Obligated to Date: $ 24,849
  • FY 2015=$24,849
Start Date:09/01/2017
End Date:08/31/2019
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:BSF: 2014012: Robust Solutions for Distributed Constraint Optimization Problems
Federal Award ID Number:1810970
DUNS ID:068552207
Parent DUNS ID:068552207
Program:ALGORITHMIC FOUNDATIONS
Program Officer:
  • Tracy J. Kimbrel
  • (703) 292-7924
  • tkimbrel@nsf.gov

Awardee Location

Street:CAMPUS BOX 1054
City:Saint Louis
State:MO
ZIP:63130-4862
County:Saint Louis
Country:US
Awardee Cong. District:01

Primary Place of Performance

Organization Name:Washington University
Street:Campus Box 1045
City:St. Louis
State:MO
ZIP:63130-4899
County:Saint Louis
Country:US
Cong. District:01

Abstract at Time of Award

Distributed constraint optimization problems (DCOPs) have been shown to be useful in modeling various distributed combinatorial optimization problems, including meeting scheduling, sensor network, and power management problems. However, many of these problems are not only distributed in nature but dynamic as well. For example, a disaster rescue scenario can include dynamic events like the collapse of buildings, detection of new survivors, and spread of fires. Previous attempts to cope with dynamism in DCOPs have focused on reactively finding a new solution when an event occurs. In this project, the PI will take a proactive approach by taking possible future events into consideration when searching for solutions. This research will result in (1) a newly designed Robust DCOP (R_DCOP) model that will include a probabilistic scheme representing the likelihood of dynamic events; and (2) R_DCOP algorithms that will address the stochastic elements of the problem. The broader impacts of this research project are two fold: (1) Through this research, the PI will build the foundations for a general robust DCOP model that can be applied in dynamic environments and spur deployment of DCOP algorithms in the real world; and (2) This project will support broadening participation of underrepresented students at NMSU, a minority- and Hispanic-serving institution.

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