System design is an exercise in sequential decision-making, with the objective of developing resilient and affordable systems. Throughout the design process, engineering and program managers must balance several competing objectives, such as ensuring design feasibility, minimizing cost, schedule, and performance risk, while simultaneously achieving stakeholder value. Thus, engineering and program managers require design and analysis methods enabling complex and multiobjective design decisions under uncertainty. Unfortunately, there exists limited research providing quantitative methodologies specifically enabling program management decisions using quantitative set-based design. We, therefore, present a quantitative set maturation and uncertainty resolution decision methodology using value-focused multiobjective decision models and model-based engineering practices. This methodology assesses and quantifies uncertainty regarding stakeholder value, cost, requirements, and design maturity for each design set. These metrics facilitate the calculation of the program manager value, which when combined with design set feasibility entropy, enable tradeoff analysis informing design maturation and uncertainty resolution prioritization decisions. We develop and demonstrate our methodology using a model-based unmanned aerial vehicle case study implemented in the ModelCenter modeling environment. This methodology provides program managers an efficient, cost effective, and defensible approach to inform system design maturation and uncertainty resolution decisions enabling the development of resilient and affordable systems.

Developing nation-conflict forecasting models is of vital importance to international resource allocation strategies that affect regional and worldwide security and stability. A nation in conflict may evolve very differently to one that is not. Using a unique set of open-source data, a suite of region-specific models are developed that predict nation-state transitions into and out of violent conflict. Conflict transition predictor variables differ not only between the regions, but also within each region dependent on if a nation is identified as in conflict or not. Accurate modeling of complex regional environments is achieved with parsimonious and interpretable models. While we ensure the causal variables of the previous study are considered for inclusion in the models developed, this paper focuses on the ability to accurately predict conflict over individual independent variable analysis.

System designers, analysts, and engineers use various techniques to develop complex systems. A traditional design approach, point-based design (PBD), uses system decomposition and modeling, simulation, optimization, and analysis to find and compare discrete design alternatives. Set-based design (SBD) is a concurrent engineering technique that compares a large number of design alternatives grouped into sets. The existing SBD literature discusses the qualitative team-based characteristics of SBD, but lacks insights into how to quantitatively perform SBD in a team environment. This paper proposes a qualitative SBD conceptual framework for system design, proposes a team-based, quantitative SBD approach for early system design and analysis, and uses an unmanned aerial vehicle case study with an integrated model-based engineering framework to demonstrate the potential benefits of SBD. We found that quantitative SBD tradespace exploration can identify potential designs, assess design feasibility, inform system requirement analysis, and evaluate feasible designs. Additionally, SBD helps designers and analysts assess design decisions by providing an understanding of how each design decision affects the feasible design space. We conclude that SBD provides a more holistic tradespace exploration process since it provides an integrated examination of system requirements and design decisions.