Award Abstract # 2410949
Collaborative Research: Elements: A Computational and Data-Capable Environment for Stochastic Simulation Optimization

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: NORTH CAROLINA STATE UNIVERSITY
Initial Amendment Date: July 14, 2024
Latest Amendment Date: July 14, 2024
Award Number: 2410949
Award Instrument: Standard Grant
Program Manager: Purushotham Bangalore
pbangalo@nsf.gov
 (703)292-7937
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: July 15, 2024
End Date: June 30, 2027 (Estimated)
Total Intended Award Amount: $88,864.00
Total Awarded Amount to Date: $88,864.00
Funds Obligated to Date: FY 2024 = $88,864.00
History of Investigator:
  • Sara Shashaani (Principal Investigator)
    sshasha2@ncsu.edu
Recipient Sponsored Research Office: North Carolina State University
2601 WOLF VILLAGE WAY
RALEIGH
NC  US  27695-0001
(919)515-2444
Sponsor Congressional District: 02
Primary Place of Performance: North Carolina State University
2601 WOLF VILLAGE WAY
RALEIGH
NC  US  27695-0001
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): U3NVH931QJJ3
Parent UEI: U3NVH931QJJ3
NSF Program(s): OE Operations Engineering,
Software Institutes
Primary Program Source: 01002425DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 073E, 077E, 077Z, 7569, 8004
Program Element Code(s): 006Y00, 800400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041, 47.070

ABSTRACT

This award investigates expanded and improved use of stochastic simulation models for optimal decision making under uncertainty. Simulation optimization (SO) can guide decisions that effectively hedge against risk, thus greater adoption will have many practical benefits across problems of importance to society in, for example, healthcare, transportation, and finance. The award addresses the lack of well-developed cyberinfrastructure for SO, which has hindered progress in the design and testing of efficient and reliable software for solving SO problems known as solvers. Significant steps will be taken to enhance the "SimOpt" testbed of SO problems and solvers to make it more powerful, widely applicable, aligned with emerging data-driven applications, and integral to the research community. Wider use of SimOpt through online content and tutorial workshops will foster more rigorous and reproducible experimentation in SO for researchers and practitioners in different fields and yield high-performing solvers for practical use. The improved library will also provide carefully curated resources for simulation educators to incorporate into their teaching efforts at all levels.

Research completed for this project will help SimOpt achieve its full potential by improving the existing code base and increasing interoperability, expanding the kinds of experiments and analyses that can be carried out, and extending the role data plays in driving the library's models and problems to open up new frontiers in methodology and algorithm design. The next generation of SimOpt will accelerate advances in SO, including solver development and testing, more extensive experiments comparing new solvers to the state of the art, and hyper-parameter tuning to improve solver performance. The work will create a new data-centered capability in SimOpt that enables more comprehensive study of trace-driven simulation and an empirical risk minimization capability that bridges to closely related areas in machine learning. These data-centered initiatives will enable researchers from diverse fields to better identify and tackle critical open problems in calibration, empirical risk minimization, and distributionally robust optimization. The resulting cyberinfrastructure will enable significant developments in SO solver capabilities, leading to enhanced use of these powerful engines in applications and intellectual bridges to adjacent research communities.

This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Operations Engineering program in the Division of Civil, Mechanical and Manufacturing Innovation within the NSF Directorate for Engineering.

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.

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