Panos Y. Papalambros
Optimal Design Laboratory • University of Michigan
I work in design optimization of products and systems, and in advancing design as a holistic yet scientific discipline, integrating practice-based thinking with science-based understanding. Through my research and teaching, I aimed to establish the scholarship and use of mathematical design optimization as a standard tool in modern design practice. In extensive collaboration with industry and government agencies, I worked on design optimization for advanced automotive systems, including electric and hybrid powertrains and structural design, and linked them with product development, commercial, and regulatory decisions to derive business and government policies. The Analytical Target Cascading (ATC) method developed in our Optimal Design Laboratory is the first proven globally convergent multi-level coordination algorithm for multidisciplinary optimization, considering both organizational and computational complexity. In our design science research we studied design preference elicitation and modeling, and linked engineering design models with those from marketing, behavioral, and social science.
James B. Angell Distinguished University Professor • Donald C. Graham Professor of Engineering • Professor of Mechanical Engineering • Professor of Integrative Systems + Design, College of Engineering • Professor of Architecture and Urban Planning • Professor of Art & Design •Director, Optimal Design (ODE) Laboratory.
Editor-in-Chief, Design Science Journal
Cambridge University Press
Board Member and Past President (2017-19),
Contact: +1 (734) 647-8401 | pyp at umich dot edu
Cambridge University Press, New York, 2017 (3d ed.), 2000 (2d ed.), 1988 (1st ed.).
The Third Edition has thoroughly updated material and includes two new chapters on non-gradient methods and systems design optimization. Visit the dedicated site for instructors and students.
All artifacts surrounding us are the results of designing. Creating these artifacts involves making a great many decisions, which suggests that designing can be viewed as a decision-making process. An abstract description of the artifact using mathematical expressions of relevant natural laws, experience, and geometry is the mathematical model of the artifact. This model may contain many alternative designs, so criteria for comparing these alternatives can be introduced in the model. Within the limitations of such a model, the best, or optimum, design can be identified with the aid of mathematical methods.
Research
ORCID • LinkedIn • Google Scholar • Scopus • Publons • ResearchGate • Academia.edu
Design Optimization
Design optimization evolved in parallel to operations research as a way to codify and support design decisions mathematically. Design thinking emerged as a way to describe a user-centered design process that seeks to unpack the core values behind design decisions. In our modern definition of Design Science as the field that studies the creation of artifacts and their embedding in our physical, psychological, economic, social and digital environments, these two approaches are merging. In persepctive, my research reflects this evolution in design optimization that links the engineering, business, computer, behavioral, social, and public policy sciences, primarily through mathematical modeling while explicitly recognizing the limitations of this design paradigm.
Over four decades we have studied a variety of topics in design optimization and design science broadly classified below. See the research section for more information.
Application Domains: Automotive Systems, especially hybrid and electric powertrains, multivehicle systems • Electromagnetic Systems, especially antennas • Manufacturing • Structural Design • Architectural design •
Optimal Design Theory and Algorithms: Multidisciplinary Design Optimization (MDO) • Smart Design: Combined optimal design and control • Global, parametric, mixed-discrete, and multiobjective optimization • Monotonicity Analysis and Model Boundedness • Optimal design under uncertainty • Configuration/Topology Design.
Product Design and Decision Making: Preference elicitation • Machine Learning and Crowdsourcing • Sustainability, Emotional Design, and Behavior Modification • Aesthetics and Proportionality
Systems Design and Product Development: Analytical Target Cascading (ATC) and analytical target setting • Decomposition and coordination strategies for complex systems • Design for Market Systems: Integration of business, marketing, engineering, public policy and economic considerations • Product platforms, portfolios, and product lines. • Product-Service Systems • Sustainable design of products and systems • Systems Design Thinking
Some current projects:
Design for Sustainable Development: In our AFRICA-DESIGN@UM project we explore how can design contribute to sustainable development in the rapidly growing African countries. We apply system design optimization to Integrated Natural Resource Conservation and Development (INRCD) projects, currently in Uganda.
System Design Thinking: What is "design thinking" in a large design organization? What are the attributes of a system design thinker? How do design organizations coordinate their distributed decisions to meet overall mission/system goals?
Power Differentials in Co-Design: How do we include marginalized individuals and communities in the design process of products and systems that direct affect them?
Select Courses
(ME 455/DESCI 501)
A benchmark, holistic, and intense design experience. Artifact design is addressed from a multidisciplinary perspective that includes engineering, art, psychology, ergonomics, marketing, and economics. Includes several rounds of prototyping.
(DESCI 502)
Exploration of the science behind the design process and its elements, including the psychology of creativity and decision making, systems thinking, interaction and coordination of decisions in multi-disciplinary design settings.
(ME 555/MFG 555)
Project-based introduction to the mathematical modeling and solution algorithms for engineering design optimization problems., including model analysis, gradient and non-gradient methods and system optimization.
(ME 250)
A complete introductory experience to mechanical design and production. Basics in visual thinking, engineering drawing, machine anatomy, manufacturing: processes, materials, engineering analysis and prototyping.