Our Research

The Design SPACE Lab conducts research that looks into how designers can conceive, analyze, choose, and create more sustainable products and systems. This work is multi-disciplinary in nature, and it combines elements and models from engineering, systems modeling, optimization, economics, behavioral science, and management science to help designers, managers, consumers, and policy-makers make more sustainable decisions that account for their complex environments.

Research Areas

The current Design SPACE research includes the following thrust areas: Design and optimization methods, systems simulation and policy, and systems and design education. 

In the area of design and optimization methods, we are developing and testing new design methods and tools to support tradeoff exploration and optimal decision making. Recent projects in this area include a new framework for holistic sustainable design of consumer products, an international collaboration to develop a research roadmap for sustainable design, multidisciplinary design optimization (MDO) of an aircraft model, MDO of a military artillery system, and scheduling optimization for maintence staff assignments. MDO is an approach to designing complex products or systems where the models and tools used to analyze the system span a number of different disciplines. For example, when optimizing the design of an aircraft, we need to concurrently analyze the aerodynamics, structural integrity, and engine performance. This is particularly challenging because the design of each of these subsystems affects the performance of the others, and usually the subsystem models are created by and understandable to different individuals or departments within an organization. Our research looks into how to support better system integration and decision-making through numerical optimization in complex design situations and other decision scenarios.

Our systems simulation and policy research involves integrating models from different disciplines to support design, strategy, and policy decision making. Recent projects include agent-based simulation of a sustainable electricity grid, market system simulation of passenger cars, and system dynamics modeling of sustainable consumer product design and policy. This research is founded in the disciplines of decision-based design and design for market systems, which quantify stakeholder preferences and mathematically model their preferences, using knowledge of the economic utility-maximizing behavior of those individuals to make optimal decisions. This creates a framework for businesses and governments to optimize strategies and policies to maximize their goals. For example, if we want to decrease our national or global fuel consumption and emissions from automobiles, we need to change the ways that cars are designed, the ways that companies market and price their vehicles, the ways that consumers purchase and use vehicles, and the ways that policy-makers regulate and incentivize the market. Understanding these interactions can help us anticipate market system reactions, improve the ways that decisions are made, and ultimately lead to more sustainable outcomes.

We also take a research approach toward improving systems and design education. Our research in this space aims to understand how people at different levels (K-12, undergraduate, graduate, and professional) learn and practice systems thinking, sustainability, and design, as well as the development and testing of new educational methods and tools. Projects in this area include a study on how students understand and learn market-driven design concepts, an investigation of student attitudes and behaviors when they are faced with a sustainable design task, and concept mapping with students and practitioners regarding their perceptions of sustainability, and a review and roadmap for the future of systems engineering education. The findings of this work are directly implementable into classrooms and training programs to improve the ways that people learn and retain these important concepts.