Panos Y. Papalambros
Optimal Design Laboratory • University of Michigan
James B. Angell Distinguished University Professor Emeritus • Donald C. Graham Professor Emeritus of Engineering • Professor Emeritus of Mechanical Engineering • Professor Emeritus of Integrative Systems + Design, College of Engineering • Professor Emeritus of Architecture and Urban Planning • Professor Emeritus of Art & Design •Director, Optimal Design (ODE) Laboratory.
Cambridge University Press, New York, 2017 (3d ed.), 2000 (2d ed.), 1988 (1st ed.).
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.
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 perspective, 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.
Optimal Design Theory and Algorithms: Artificial intelligence in optimal design • Optimal configuration and topology design • Co-Design: Optimal design and optimal control • Monotonicity analysis • Multiobjective design optimization • Design under uncertainty
Application Domains in Systems and Product Design: Architectural design • Automotive systems, especially hybrid and electric powertrains • Electromagnetic systems, especially antennas • Manufacturing and design integration • Structural design
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, and socio-technical goals?
Power Differentials in Co-Design: How do we include marginalized individuals and communities in the design process of products and systems that directly affect them?
(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.
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.
A complete introductory experience to mechanical design and production. Basics in visual thinking, engineering drawing, machine anatomy, manufacturing: processes, materials, engineering analysis and prototyping.