| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | July 12, 2011 |
| Latest Amendment Date: | July 12, 2011 |
| Award Number: | 1130761 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Donald Hearn
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2011 |
| End Date: | August 31, 2015 (Estimated) |
| Total Intended Award Amount: | $197,269.00 |
| Total Awarded Amount to Date: | $197,269.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
W5510 FRANKS MELVILLE MEMORIAL LIBRARY STONY BROOK NY US 11794-0001 (631)632-9949 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
NY US 11794-3600 |
| 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
The research objective of this award is to create new simulation optimization algorithms that combine rigorous theoretical performance guarantees with the robust empirical behavior of a class of random search techniques called the model-based methods. The research approach is based on integrating the principle of the well-known Expectation-Maximization (EM) algorithm from the field of statistics into model-based methods. In particular, through exploiting a novel connection to the EM algorithm, this research will investigate a unifying framework to design and implement new model-based algorithms for solving a broad class of simulation optimization problems with very modest computational effort. These algorithms will be studied in terms of their properties (such as convergence and convergence rate) using a fusion of theories and tools from EM, stochastic approximation, and Quasi-Newton methods. A variety of applications from biostatistics to electric power systems will also be tested for the purposes of evaluating the practical utility of the developed techniques and algorithms.
If successful, the resulting techniques will have applicability in a wide array of industry and science sectors. Through collaboration with bio-statisticians, the developed algorithms will be applied to optimal drug dose-response experimental designs, with potential benefits to health care. In addition, the intended applications to electric power systems will also promote synergy among different disciplines. The research resulting from this project will be disseminated through publications, software development, and participation at national and international conferences. This award will also be closely integrated with the education and training of students in mathematical science and engineering by incorporating new developments into the advanced courses taught by investigators at different institutions, and promoting the participation of female students in research.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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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.
The objective of this research is to develop new optimization algorithms for improving the performance of complex systems that require the use of simulation to estimate their performance. The focus is on integrating ideas and tools from the field of statistics into simulation-based optimization to study new random search techniques that combine rigorous theoretical performance guarantees with robust empirical behavior.
With the support of this NSF grant, we have developed two general frameworks to design and implement new algorithms for solving a broad class of simulation optimization problems. One framework exploits the connection of a class of random search algorithms to a well-known method in statistical estimation, which leads to a novel technique for finding improved optimization solutions at significantly reduced computational effort. The other framework links random search algorithms with classical gradient-based approaches and combines the robustness feature of random search with the fast convergence speed of gradient methods by exploiting structural knowledge of the problem. A number of algorithms have been developed under these two frameworks. These algorithms have been studied in terms of their properties such as convergence and convergence speed, followed by testing their performance on real design problems.
Because of the generality of the proposed frameworks, the resulting techniques have applicability in a wide array of industry and science sectors. In particular, we have made considerable advances in applying the proposed optimization techniques to solving problems in electric power systems. These include successful applications of the developed algorithms to feeder reconfiguration problems, voltage and reactive power control problems, and unit commitment problems for the purposes of reducing power system losses and increasing energy efficiency. This research project has also been closely integrated with the education of students in mathematical science and engineering. It has provided one Ph.D. student with full financial support and other student participants with training in optimization, simulation, and applied probability. The results from this project have been disseminated through publications in high-quality journals and presentations at national and international conferences.
Last Modified: 10/01/2015
Modified by: Jiaqiao Hu
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