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Employing Optimization to Streamline Scenario Generation Across a System of Systems M&S Enterprise

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-19-C-7159
Agency Tracking Number: B2-2592
Amount: $1,000,000.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: MDA16-006
Solicitation Number: 16.2
Timeline
Solicitation Year: 2016
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-05
Award End Date (Contract End Date): 2021-08-04
Small Business Information
2241 17th Street
Boulder, CO 80302
United States
DUNS: 128005423
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Benjamin Thengvall
 VP of Government Solutions
 (303) 447-3255
 thengvall@opttek.com
Business Contact
 James Kelly
Phone: (303) 447-3255
Email: kelly@opttek.com
Research Institution
N/A
Abstract

A simulation optimization tool can exploit existing BMDS simulation models to streamline the scenario generation analysis process. Our Phase II project will focus on using a simulation optimization approach to solve the problem of generating a minimal collection of test scenarios, using the minimum number of shared scenario components that meets a required set of test objectives. Each individual scenario taken through the end to end scenario generation process involves substantial effort including discussion, documentation, and manual scenario generation in high fidelity tools. Likewise, scenario components require a great deal of time and effort to prepare for the test event. We will explore the use of an optimization tool with medium fidelity digital simulation models early in the scenario generation process to minimize the required test scenarios and scenario components. Reducing the number of test scenarios and scenario components has the potential to significantly reduce the end to end scenario generation timeline and associated costs. We will leverage the SBIR-derived OptDef simulation optimization and analysis tool to prototype our scenario generation optimization approach. We will also explore ways for this technology to be used in digital event test planning and also exercise event test planning to enable maximum benefit. Approved for Public Release | 19-MDA-9932 (21 Feb 19)

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

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