| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | April 30, 2008 |
| Latest Amendment Date: | April 30, 2008 |
| Award Number: | 0800688 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Edwin Romeijn
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | July 1, 2008 |
| End Date: | June 30, 2013 (Estimated) |
| Total Intended Award Amount: | $267,045.00 |
| Total Awarded Amount to Date: | $267,045.00 |
| 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: |
341 PINE TREE RD ITHACA NY US 14850-2820 |
| 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): |
SERVICE ENTERPRISE SYSTEMS, 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
Collaborative Research: Inference, Analysis, and Assessment in Simulation Optimization
Abstract
This grant proposes three main areas of work on simulation optimization (SO) problems, which are optimization problems where the objective function and constraints involved can only be observed through a stochastic simulation. First, many SO problems possess structure such as convexity or unimodality that, if detected, can improve one?s understanding of the problem itself, and be exploited in selecting solution algorithms. Numerical methods will be developed to detect such structure. Second, performance measures, and methods for efficiently computing them, will be developed to enable theoretically sound comparisons of the performance of SO algorithms on test problems. Third, a testbed of SO problems will be developed.
If awarded, the ability to numerically detect problem structure will greatly improve understanding of one?s problem formulations, and allow greater use of specialized algorithms that exploit structure. This could also lead to users formulating problems to adhere to those structures, with the result that many new subclasses of SO problems might be created. The testbed, along with appropriate performance measures, should help to encourage algorithm comparisons and development. We might then be able to tackle far larger SO problems than is possible today. The results of the research will find application in areas such as emergency services, transportation logistics, supply chain management, revenue management and potentially many other fields.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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