| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
|
| Initial Amendment Date: | June 15, 2014 |
| Latest Amendment Date: | April 18, 2019 |
| Award Number: | 1434419 |
| 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: | January 1, 2015 |
| End Date: | December 31, 2019 (Estimated) |
| Total Intended Award Amount: | $220,000.00 |
| Total Awarded Amount to Date: | $228,000.00 |
| Funds Obligated to Date: |
FY 2019 = $8,000.00 |
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
3112 LEE BUILDING COLLEGE PARK MD US 20742-5100 (301)405-6269 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
4305 Van Munching Hall College Park MD US 20742-1871 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): |
OE Operations Engineering, OPERATIONS RESEARCH |
| Primary Program Source: |
01001920DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Simulation is widely used in many industrial settings, from manufacturing and supply chain management to service systems, including health care, transportation, and financial services. Due to the complexity of many of these systems, however, computation has often been a limiting factor in solving large-scale problems based on simulation models, even with the continuing advances in computing power. This award supports fundamental research leading to new algorithms that would improve the efficiency of finding optimal decisions for many problems in the manufacturing and service industries mentioned above, and thus lead to direct benefits to the U.S. economy and society. The research involves mathematical models, computing, applied probability, and statistics.
Direct gradient estimation techniques such as perturbation analysis and the likelihood ratio method provide computationally efficient methods for obtaining unbiased gradient estimators without the need for resimulation. Such estimators are the basis for gradient-based search procedures used in many simulation optimization algorithms. However, the resulting algorithms use only the gradients, consistent with their application in the deterministic optimization setting, where the gradients are exact so there is no value gained in using the objective function (or performance measure) values themselves for performing gradient search. On the other hand, in the stochastic setting, the gradient estimates are noisy, which means that using the function values to provide additional information on estimating the gradient may be beneficial. The proposed research explores new methods for incorporating direct gradient estimates from stochastic simulation into existing simulation optimization techniques, specifically response surface methodology and stochastic approximation. The goals of the research include: (i) developing new more effective algorithms, (ii) proving convergence of the resulting algorithms, (iii) analyzing finite-time properties of the algorithms, and (iv) providing practical implementation guidelines based on both theory and empirical numerical testing. Thus, in addition to algorithmic advances, new theory will likely be needed to provide guidance as to the settings in which the new algorithms are likely to provide additional benefit.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
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.
Our project addressed a general setting where a decision maker must make multiple sequential decisions over a time horizon involving random (or uncertain) sequential outcomes. This setting covers a wide range of problems, from production planning to transportation systems to games. We are specifically interested in problems where a simulation model is available. For such problems, we developed several computationally efficient algorithms (e.g., a new 2nd-order Secant-Tangents AveRaged (STAR) stochastic approximation algorithm that uses both direct and indirect gradients) to provide optimal decision support and demonstrated the algorithms on applications from inventory control to financial engineering to two-person games. Our research involves mathematically rigorous analysis for developing the algorithms where important theoretical properties are proved, and numerical studies to test the algorithms. As an illustration of one of the algorithms, we have a Web-based simulation written in Java that is available to the general public (on the PI's Web site) to see how such algorithms perform on a simple game of tic-tac-toe. The research results have been published in numerous prestigious peer-review journals and presented at all of the leading conferences in the field. The research project has involved several faculty and PhD students, a postdoctoral fellow, and two undergraduate students.
Last Modified: 03/20/2020
Modified by: Michael C Fu
Please report errors in award information by writing to: awardsearch@nsf.gov.
