RESEARCH AREAS

We have studied a variety of topics in design optimization and design science, including:

Product design and decision making 
Preference elicitation, preference structures and assessment • Learning algorithms and mathematical models of crowdsourcing • Emotional design quantification: Aesthetics and proportionality, perception of sustainability behavior modification through design

System design optimization and product development
Decomposition and coordination strategies for large-scale systems, multidisciplinary design optimization (MDO) • Design for market systems: Enterprise-wide business, marketing, engineering, public policy and economic considerations • Analytical target cascading and analytical target setting • Optimal design of product platforms, portfolios, and product lines

Optimal design theory and algorithms
Monotonicity analysis • Global, parametric, mixed-discrete, and Pareto optimization • Distributed, multilevel, multidisciplinary system optimization Artificial intelligence, expert systems and nonlinear mathematical optimization • Optimal design under uncertainty • Co-design: Combined optimal design and optimal control

Applications in systems and product design
Analytical craftsmanship • Architectural design • Automotive systems, especially hybrid and electric powertrains • Electromagnetic systems, especially antennas • Manufacturing and design integration • Structural design • Sustainable products and systems

CURRENT PROJECTS

For more information, see http://ode.engin.umich.edu/research.html

Active Preference Elicitation and Crowdsourcing
If you used a search engine, someone haation in psychology and marketing requires a tradeoff between limited resources to obtain human behavior observations and the desire for an accurate model that captures the complexity of human preference. In our research, we study user-interactive elicitation processes. We employ intelligent query generation mechanisms to press just learned a bit more about your preferences. Preference elicitation through machine learning is a strategy companies use to understand the market and target the right products to the right people. Optimal product design has a strong connection with human preference modeling. Creating mathematical models through preference elicitent choices adaptively based on previous observations, similar to strategies used in active learning and adaptive conjoint analysis. We use online or mobile applications, such as the interactive vehicle design, to collect and analyze preference data.

Optimal Hybrid Powertrain Architecture Design
There are several hybrid electric vehicles (HEVs) in the commercial market with a variety of powertrain architectures. Generally, they have much better fuel economy than their conventional IC engine counterparts. But do these HEVs have the best powertrain architecture for their target use? One can start with an existing powertrain design and optimize it changing some of its parts to account for changes in targeted use or duty cycles. In this research, we develop a modeling environment for general powertrain architecture representation using bond graphs. Changing the connections between components in a graph generates different architectures. We explore solutions within this large combinatorial problem using implicit enumeration and heuristic evaluations to select the best design architecture for a particular vehicle or group of vehicles in terms of fuel economy and performance.

Product and Service Design for Market Systems
Low price tablets have become very marketable even though their manufacturing cost is much higher. Why would manufacturers launch unprofitable products? This is because customers are typically required to “bundle” the manufacturer’s services (e.g., apps, e-books, movies) with the manufacturer’s specific product. The firm expects to make up its production costs and make a profit through the sale of paid services over time. In our research, we create the modeling framework called Enterprise-driven Product Service System to capture the value of product and associated services together in order to maximize profit. We use models from marketing and engineering to extend previous models of design for market systems to include service design.

Persuasive Design for Behavior Change
Everyday we hear about overflowing landfills, declining resources, and apparent global warming. We may worry but do we really want to be sustainable? If so, then why do we install compact florescent bulbs, but forget to turn them off when leaving the room? Why do we drive fuel-efficient cars, but use them unnecessarily? A seemingly technical challenge is oftentimes behavioral. Studies show that merely focusing on technological efficiency without recognizing user behavior is likely to fail in meeting sustainability challenges. In this research, we investigate the influence of formal design properties (like color, shape, or size) and meaningful design properties (metaphors) in encouraging behavior change. Through quantitative and qualitative methods from psychology and design, we investigate persuasive strategies in product design that can trigger behavioral changes. We follow an interdisciplinary approach merging social psychology and industrial design, and we develop guidelines, heuristics and strategies to design products that elicit environmentally responsible behavior. A current case study has examined how design of napkin dispensers in local coffee houses has changed consumption patterns.

Socio-Technical Analysis of Interdisciplinary Interactions in Complex Engineered Systems
Does the design of an organization affect the design of the products and systems it creates? Interdisciplinary interactions that take place during the research, development, and early conceptual design phases in the engineering of large-scale complex engineered systems (LaCES) such as aerospace vehicles. These interactions that occur throughout a large engineering development organization, become the initial conditions of the systems engineering process ultimately leading to the development of a viable system. In this research, we explore challenges and opportunities regarding social and organizational issues that emerge from qualitative studies using ethnographic and survey data. Analysis in a current case study reveals several socio-technical couplings between the engineered system and the organization that creates it. Survey respondents noted the importance of interdisciplinary interactions and their benefits to the engineered system as well as substantial challenges in interdisciplinary interactions. Noted benefits included enhanced knowledge and problem mitigation and noted obstacles centered on organizational and human dynamics. Findings suggest that addressing the social challenges may be a critical need in enabling interdisciplinary interactions during the development of LaCES.