| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | March 9, 2012 |
| Latest Amendment Date: | March 9, 2012 |
| Award Number: | 1200315 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Georgia-Ann Klutke
gaklutke@nsf.gov (703)292-2443 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | July 1, 2012 |
| End Date: | June 30, 2016 (Estimated) |
| Total Intended Award Amount: | $250,515.00 |
| Total Awarded Amount to Date: | $250,515.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
341 PINE TREE RD ITHACA NY US 14850-2820 (607)255-5014 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
School of ORIE, 230 Rhodes Hall Ithaca NY US 14853-3801 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | OPERATIONS RESEARCH |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
This award provides funding to advance the design of algorithms for solving simulation-optimization (SO) problems on parallel computing platforms. SO problems are optimization problems where the objective function and constraints can only be observed through a stochastic simulation. Virtually all current algorithms for solving SO problems assume a single processor as the computing platform. However, the trend in computing devices is towards multi-processor computers, not just at the laptop/desktop level, but all the way up to cloud computing environments. This research will explore how to design algorithms for solving SO problems that exploit such environments, to attempt to return high-quality solutions at a reasonable cost and within a reasonable amount of time.
If successful, the results of this research will lead to a new line of research - parallel SO - with ensuing improvements in the design and implementation of SO algorithms on parallel computing platforms, thus making this currently computing-intensive technology much more accessible and effective. SO already holds an important place in application fields, as evidenced by the variety of SO problems in an existing testbed
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This research explores the use of parallel computing to solve "simulation optimization problems." A simulation optimization problem involves the use of computer models that mimic the dynamics and uncertainties associated with real-life systems to attempt to identify decisions that improve the operation and design of those systems. For example, our work has been extensively used to help improve the operations and design of CitiBike, the bike-sharing system in place in New York City. It has also been used to help plan operations and select bases for Ornge, the air-ambulance provider in Ontario, Canada.
The focus of the work in this proposal is to design and develop computer programs to enable the solution of simultion-optimization problems using the power of parallel computing, including high-performance computing and cloud computing. We have successfully devised such programs that have solved problems that are larger than any previously solved by a factor of approximately 100 (in terms of number of potential solutions). These algorithms provide statistical guarantees that the reported optimal solution is close to the truly-best solution. These guarantees are stronger than those provided by most algorithms previously designed for these problems. One of our algorithms has been adapted for use by a commercial simulation software provider.
In addition, the work has provided training for a large number of graduate and undergraduate students, and a postdoctoral scholar.
Last Modified: 07/13/2016
Modified by: Shane G Henderson
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