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A Scalable, Flexible and Efficient Simulation Optimization Tool for Ballistic Missile Defense

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0006-09-C-7153
Agency Tracking Number: B083-036-0366
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: MDA08-036
Solicitation Number: 2008.3
Timeline
Solicitation Year: 2008
Award Year: 2009
Award Start Date (Proposal Award Date): 2009-03-27
Award End Date (Contract End Date): 2009-09-27
Small Business Information
15400 Calhoun Drive Suite 400
Rockville, MD 20855
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Renato Levy
 Chief Scientist
 (301) 294-5241
 rlevy@i-a-i.com
Business Contact
 Mark James
Title: Director, Contracts and Proposals
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
N/A
Abstract

IAI propose to develop an innovative simulation optimization tool for missile defense. This tool will be the synergy of an integrated process diagram, simulation optimization algorithm of combining Nested Partition and Optimal Computing Budget Allocation, and distributed computation. Representing each simulation model explicitly as an interoperable object, we propose to use an interconnected process diagram to represent the system-level simulation optimization problem for missile defense. In the process diagram, each node represents a process to simulate and these nodes are connected to form the system-level decomposition of a complex problem. Inside each process, multiple models, not limited to simulation models, can be placed. The connections between processes are the mappings of variables. The process diagram will integrate various simulation and analytical models using generic interfaces. Our proposed simulation optimization algorithm integrates two state-of-the-art techniques: Nested Partition and Optimal Computing Budget Allocation. Since the dependencies between simulation models and simulation replications are limited, we propose to use distributed computation with automatic control of computational resources to speed up the simulation process. In the distributed computation, simulation models grouped in simulation replications can run concurrently.

* Information listed above is at the time of submission. *

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