SBIR-STTR Award

Over the last decade, the systems engineering research community has spent significant effort targeting improvements in approaches for designers interested in trading between multiple competing objectives, such as mobility and survivability. Methods such as set-based design and tradespace exploration now leverage advanced optimization and analysis techniques, reducing computational and cognitive burdens on designers and analysts, while enabling the consideration of large potential design spaces. Making sense of the resulting large datasets is made possible through targeted use of ion, analysis, and automation. The degree of automation that can and should be leveraged when making tradeoffs varies based on the nature of uncertainties facing a given case. Acknowledging the power of an adaptive capability, instead of using a one-size fits all automated tradeoff approach, we propose an approach that can be tailored across an automation spectrum. Such tailoring allows for discovery of the impact different methods for combining and trading metrics have on the recommendation of best designs. Our proposed approach and tool, the Tailorable Tradespace Analysis of Mobility and Survivability for Ground Vehicles (TTAMS-GV), will provide new insights to ground vehicle designers that are robust to variations in mission needs, technologies, and priorities, common uncertainties faced during early design decision-making.

The DoD is faced with the national security challenge of developing adversary-exceeding military capabilities, across a variety of possible scenarios and contexts, over an enduring period into the future. This challenge is highly complex but can be addressed using state-of-the-art decision sciences, analytics, and artificial intelligence at both the system and the portfolio capability levels. This enables identifying, evaluating, and exploring complex trade-offs across performance, life-cycle costs, acquisition schedules, and procurement quantities, among other factors. Multi-Attribute Tradespace Exploration (MATE) is a methodology that enables value-driven and data-supported exploration and analysis of the relationships between various solutions, benefits, and costs. MATE provides a structured analytic framework that incorporates stakeholder values, contextual variations and uncertainties, broad ranging future warfighting performance evaluation modeling, generation of large sets of alternatives, and exploration of trade-offs across those alternatives with tested algorithms and advanced interactive visual analytics. MATE also provides the basis for integrating trusted data that will enable application of artificial intelligence (AI) techniques to collaboratively explore extremely complex search spaces not previously possible. This project will build a working software platform that will enable and enhance applications of MATE to both system and portfolio-level acquisition analytics challenges of critical importance to the DoD.