No abstract

Increasing system complexity has provided the impetus to develop new and novel systems engineering methodologies. One of these methodologies is set‐based design (SBD), a concurrent design methodology well suited for complex systems subject to significant uncertainty. Since the 1990s, numerous private, public, and defense sector design programs have successfully implemented SBD. However, concerns regarding SBD's complexity, tendency toward qualitative methods, and lack of quantitative tools have limited its use. To address these issues, our research surveys 122 refereed journal articles and conference papers to assess SBD's state‐of‐practice and identify relevant research opportunities. To accomplish these tasks, we perform a structured literature review to identify and assess relevant and influential research. We found that SBD's state‐of‐practice relies heavily upon decision and tradespace analysis with increasing emphasis on uncertainty modeling and MBSE. We found that the majority of SBD research consists of quantitative methodologies focusing on component and small system applications. We also found that complex system applications used mostly qualitative methodologies. We identify SBD research opportunities for requirements development, MBSE, uncertainty modeling, multiresolution modeling, adversarial analysis, and program management. Finally, we recommend the development of a comprehensive SBD methodology and toolkit, suited for complex system design across all stages of the product development life cycle.

Engineering complex systems is an exercise in sequential multiobjective decision making under uncertainty. One method for handling this complexity and uncertainty is set-based design (SBD). SBD is a concurrent engineering and management methodology that develops, analyzes, and matures numerous design options, reducing risk and delivering higher value to the stakeholders and end users. SBD accomplishes this through controlled design space convergence which reduces uncertainty and prevents premature design decisions. While SBD has been the subject of numerous scholarly articles, there is limited research providing quantitative methodologies that inform decisions enabling design maturation and convergence. We present a value of information (VOI) based methodology for multiobjective decision problems, and demonstrate its applicability for SBD decisions. We apply Bayesian decision models and information value to inform multiobjective modeling and design maturation decisions. Research contributions include: 1) a framework integrating VOI into the SBD process, 2) a multiobjective VOI method assessing a higher-resolution model's ability to reduce uncertainty, and 3) a means of informing modeling decisions by comparing multiple high resolutions models, given their usage cost and their potential to deliver information value.Finally, we demonstrate the inherent issues associated with premature decisions and traditional point-based design approaches which run the risk of selecting an alternative that later proves infeasible.

This paper examines the potential to use Model-Based Systems Engineering (MBSE) tools to perform trade-off analysis of alternative systems decisions in the system life cycle from the concept stage to the retirement stage. Specially, we searched for integrated models that automate the simultaneous evaluation of the performance, effectiveness, stakeholder value, and cost of multiple alternative system designs. We used the Web of Science to perform a literature search to identify published papers that describe the use of MBSE tools to support automated analysis of alternatives and trade-off analyses. We found very few papers that claimed to use MBSE to provide analysis of design alternatives or tradespace exploration. Based on the literature search insights, we identify and describe the required and desired capabilities to perform automated trade-off analyses of performance, effectiveness, stakeholder value, and cost for multiple system design alternatives using integrated models.

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.

Increasing system complexity requires that engineers, systems analysts, and program managers use comprehensive design methodologies to deliver affordable and resilient designs. One method is set-based design (SBD), a product development and managerial process distinctly suited for developing complex systems under uncertainty. SBD simultaneously develops, analyzes, and matures numerous potential design sets, enabling the identification of high-value, affordable, and resilient designs. Published SBD research is a rich source of both qualitative and quantitative methods. This research specifically focuses on quantitative SBD methods to apply a value of information (VOI) methodology enabling design convergence and selection. We build upon previous SBD research to enable design maturation and uncertainty reduction. Our methodology integrates design maturation and multiobjective VOI analysis into a comprehensive quantitative SBD process to guide system development from initial design concepts to the pre-production design decision. In doing so, we also provide refinements and process improvements to existing quantitative SBD methods. We demonstrate our methodology with a model-based UAV design case study using an integrated suite of system, value, and cost models. Our case study specifically focuses on the design maturation and model selection decisions enabling design space convergence.We compare our current results with those from a previous UAV case study, achieving a 41% reduction in required computation time for design space convergence. These results highlight the methodology's ability to reduce program risk and potential to improve SBD convergence efficiency.

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.

No abstract