Award Abstract # 0400287
Structured Simulation Optimization and Analysis

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: CORNELL UNIVERSITY
Initial Amendment Date: April 1, 2004
Latest Amendment Date: September 1, 2006
Award Number: 0400287
Award Instrument: Continuing Grant
Program Manager: Cerry M. Klein
CMMI  Division of Civil, Mechanical, and Manufacturing Innovation
ENG  Directorate for Engineering
Start Date: August 1, 2004
End Date: July 31, 2008 (Estimated)
Total Intended Award Amount: $299,735.00
Total Awarded Amount to Date: $299,735.00
Funds Obligated to Date: FY 2004 = $95,733.00
FY 2005 = $101,941.00
FY 2006 = $102,061.00
History of Investigator:
  • Shane Henderson (Principal Investigator)
     sgh9@cornell.edu
Recipient Sponsored Research Office: Cornell University
 341 PINE TREE RD
 ITHACA
 NY  US  14850-2820
(607)255-5014
Sponsor Congressional District: 19
Primary Place of Performance: Cornell University
 341 PINE TREE RD
 ITHACA
 NY  US  14850-2820
Primary Place of Performance
Congressional District:		 19
Unique Entity Identifier (UEI): G56PUALJ3KT5
Parent UEI:
NSF Program(s): SERVICE ENTERPRISE SYSTEMS
Primary Program Source: app-0104 
app-0105 
app-0106 
Program Reference Code(s): 9147, MANU
Program Element Code(s): 178700
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

A huge number of applications involve simulation optimization, i.e., the optimization of a function that is computed via Monte Carlo simulation. The primary goal of the research is to develop solution methods for structured simulation optimization problems. The word "structured" suggests that the problem possesses certain structural properties that can be leveraged by optimization algorithms to, for example, reduce solution time or improve the likelihood of convergence. In more detail, the primary goals of this research are as follows. First, to further develop simulation optimization methods for structured problems, where the structure may be detected and verified with minimal user input. Second, to apply those methods on a range of practical problems, with emphasis on emergency services in rural areas. Third, to create a library of simulation optimization problems that can be used to help compare proposed optimization methods, and help guide the development of new simulation-optimization algorithms.

The development of structured simulation-optimization methods will allow far larger models and associated optimization problems to be solved than is possible today. The potential list of applications is enormous, since Monte Carlo simulation is now used in a tremendous range of fields including, but certainly not limited to, emergency service planning, manufacturing, communications systems, service system design and health
care. The research explicitly includes applications to emergency service planning in rural areas, with the goal of obtaining more effective response times and coverage. Other potential benefits include improved methods for operating policy design for networks such as arise in power systems, communication systems and manufacturing. Applications also exist in health care, such as in external beam radiation treatment planning for cancer patients.

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.

(Showing: 1 - 10 of 11)
Atlason, J., M. Epelman and S. G. Henderson. "Optimizing call center staffing using simulation and analytic center cutting plane methods" Management Science , v.54 , 2008 , p.295
Chu, M., Y. Zinchenko, S. G. Henderson and M. B. Sharpe. "Robust optimization for intensity modulated radiation therapy treatment planning under uncertainty." Physics in Medicine and Biology. 50 5463?5477 , v.50 , 2005 , p.5463
Ehrlichman, S. M. T. and S. G. Henderson. "Adaptive control variates for pricing multi-dimensional American options" Journal of Computational Finance , v.11 (1) , 2008
Ehrlichman, S. M. T. and S. G. Henderson. "Finite sample performance guarantees for one-dimensional stochastic root finding" Proceedings of the 2007 Winter Simulation Conference , 2007 , p.313
Henderson, S. G. "Should we model dependence and nonstationarity, and if so how?" Proceedings of the 2005 Winter Simulation Conference. M. E. Kuhl, N. M. Steiger, F. B. Armstrong, and J. A. Joines, eds. IEEE, Piscataway, NJ. , 2005 , p.120
Kim, S. and S. G. Henderson. "Adaptive Control Variates" Proceedings of the 2004 Winter Simulation Conference. R. Ingalls, M. Rossetti, J. Smith and B. Peters, eds. IEEE, Piscataway, NJ , v.2004 , 2004 , p.621
Kim, S., and S. G. Henderson "Adaptive control variates for finite-horizon simulation" Mathematics of Operations Research , v.32 , 2007 , p.508
S. Kim and S. G. Henderson "Non-linear control variates for regenerative steady-state simulation" Proceedings of the 2007 Winter Simulation Conference , 2007 , p.430
Soumyadip Ghosh and Shane G. Henderson "Corrigendum: Behaviour of the NORTA method for correlated random vector generation as the dimension increases" ACM TOMACS , v.16 , 2005 , p.93
Steckley, S. G. and S. G. Henderson "The error in steady-state approximations for time-dependent performance measures" Stochastic Models , v.23 , 2007 , p.307
Steckley, S. G., S. G. Henderson and V. Mehrotra. "Performance measures for service systems with a random arrival rate." Proceedings of the 2005 Winter Simulation Conference. M. E. Kuhl, N. M. Steiger, F. B. Armstrong, and J. A. Joines, eds. IEEE, Piscataway, NJ. , 2005 , p.566
(Showing: 1 - 10 of 11)

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page

Top