Clark School Home UMD

ISR News Story

Michael Fu wins NSF grant for simulation-based optimal decision making

Professor Michael Fu (BMGT/ISR) is the principal investigator for a three-year, $220K National Science Foundation grant, “New Approaches for Simulation-Based Optimal Decision Making.”

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.

Related Articles:
Maryland research contributes to Google’s AlphaGo AI system
Marcus, Fu receive NSF grant for optimization research
Marcus, Fu receive NSF grant for particle filtering for stochastic control and global optimization
Researchers write about Google's AlphaGo for OR/MS Today
Gupta is PI for NSF NRI unmanned surface vehicle grant
New book by Jeffrey Herrmann: Engineering Decision Making and Risk Management
Steven Gabriel receives Humboldt Research Award
ECE Ph.D. Candidate Menon Wins Kulkarni Fellowship
Krishnaprasad, Jarzynski part of 'Information Engines' MURI
Ph.D. student James Jones wins FAA research stipend

January 15, 2015

Prev   Next



Current Headlines

Coelho, Austin and Blackburn win best paper award at ICONS 2017

Ryzhov promoted, granted ISR/Smith School joint appointment

Miao Yu named Maryland Robotics Center director

Decade of TMV research leads to never-before-seen microsystems for energy storage, biosensors and self-sustaining systems

Former ISR postdoc Matthew McCarthy earns tenure at Drexel University

New TMV supercapacitor work featured in Nanotechweb article

Jonathan Fritz promoted to Research Scientist

ISR postdoc helps develop 'nanosponge' that erases and repairs incredibly small errors

ECE Professors Abshire, Goldsman, and Newcomb Participate in ISCAS 2017

Sandborn Awarded ASME Kos Ishii-Toshiba Award

News Resources

Return to Newsroom

Search News

Archived News

Events Resources

Events Calendar