Simon W. Miller

Preference Construction, Sequential Decision Making, and Trade Space Exploration

This paper develops and explores the interface between two related concepts in design decision making. First, design decision making is a process of simultaneously constructing one’s preferences while satisfying them. Second, design using computational models (e.g., simulation-based design and model-based design) is a sequential process that starts with low fidelity models for initial trades and progresses through models of increasing detail. Thus, decision making during design should be treated as a sequential decision process rather than as a single decision problem. This premise is supported by research from the domains of behavioral economics, psychology, judgment and decision making, neuroeconomics, marketing, and engineering design as reviewed herein. The premise is also substantiated by our own experience in conducting trade studies for numerous customers across engineering domains. The paper surveys the pertinent literature, presents supporting case studies and identifies use cases from our experiences, synthesizes a preliminary model of the sequential process, presents ongoing research in this area, and provides suggestions for future efforts.Copyright

Design as a Sequential Decision Process: A Method for Reducing Design Set Space Using Models to Bound Objectives

Design is a sequential decision process that increases the detail of modeling and analysis while simultaneously decreasing the space of alternatives considered. In a decision theoretic framework, low-fidelity models help decision-makers identify regions of interest in the tradespace and cull others prior to constructing more computationally expensive models of higher fidelity. The method presented herein demonstrates design as a sequence of finite decision epochs through a search space defined by the extent of the set of designs under consideration, and the level of analytic fidelity subjected to each design. Previous work has shown that multi-fidelity modeling can aid in rapid optimization of the design space when high-fidelity models are coupled with low-fidelity models. This paper offers two contributions to the design community: (1) a model of design as a sequential decision process of refinement using progressively more accurate and expensive models, and (2) a connected approach for how conceptual models couple with detailed models. Formal definitions of the process are provided, and a simple one-dimensional example is presented to demonstrate the use of sequential multi-fidelity modeling in determining an optimal modeling selection policy.Copyright

Toward a Value-Driven Design Approach for Complex Engineered Systems Using Trade Space Exploration Tools

Design decision-making involves trade-offs between many design variables and attributes, which can be difficult to model and capture in complex engineered systems. To choose the best design, the decision-maker is often required to analyze many different combinations of these variables and attributes and process the information internally. Trade Space Exploration (TSE) tools, including interactive and multi-dimensional data visualization, can be used to aid in this process and provide designers with a means to make better decisions, particularly during the design of complex engineered systems. In this paper, we investigate the use of TSE tools to support decision-makers using a Value-Driven Design (VDD) approach for complex engineered systems. A VDD approach necessitates a rethinking of trade space exploration. In this paper, we investigate the different uses of trade space exploration in a VDD context. We map a traditional TSE process into a value-based trade environment to provide greater decision support to a design team during complex systems design. The research leverages existing TSE paradigms and multi-dimensional data visualization tools to identify optimal designs using a value function for a system. The feasibility of using these TSE tools to help formulate value functions is also explored. A satellite design example is used to demonstrate the differences between a VDD approach to design complex engineered systems and a multi-objective approach to capture the Pareto frontier. Ongoing and future work is also discussed.Copyright

Adding Value to Trade Space Exploration When Designing Complex Engineered Systems

Design decision-making involves tradeoffs between many design variables and attributes, which can be difficult to model and capture in complex engineered systems. To choose the best design, the decision maker is often required to analyze many different combinations of these variables and attributes and process the information internally. Trade Space Exploration (TSE) tools, including interactive and multidimensional data visualization, can be used to aid in this process and provide designers with a means to make better decisions, particularly during the design of complex engineered systems that have multiple, competing objectives. In this paper, we investigate the use of TSE tools to support decision makers using a Value-Driven Design (VDD) approach for complex engineered systems. A VDD approach necessitates a rethinking of TSE, and we outline and illustrate four different uses of a VDD approach to TSE. The research leverages existing TSE paradigms and multidimensional data visualization tools to identify optimal designs when using a value function for a system. A satellite design example is used to demonstrate the differences between a VDD approach to design complex engineered systems and a multiobjective approach to capture the Pareto frontier. Ongoing and future work is also discussed.

Sustainability Adoption Trend Analysis

The sustainability trend in the automotive market has been analyzed. A dataset of cars was elicited from select companies that have increased their market share in the United States automotive market over the past decade. In the first part, a linear regression model is developed to evaluate how the market share is influenced by key vehicle characteristics, and in particular, to evaluate the role of sustainability in that analysis. For the second part, game theory has been applied to see how market dynamics change in relation to sustainability moves of two competitors. The third part uses technology forecasting techniques to suggest which technology to invest. The results of the paper show that, in the long term, sustainability will be a significant factor in determining a company’s market share. Investing in manufacturing processes that reduce the cost of battery systems can support the competitiveness of hybrid vehicles but so too can investing in research and development to reduce the energy density gap between gasoline and batteries.