The purpose of this article is to provide a permanent record of the major ideas and questions raised during the panel session entitled “Future Trends in Optimization” at the 1980 Design Engineering Technical Conference in Beverly Hills, California. The panel members were Professors D. J. Wilde of Stanford University, E. J. Haug of the University of Iowa, K. M. Ragsdell of Purdue University, J. N. Siddall of McMaster University, and F. Freudenstein of Columbia University. They spoke, respectively, on Optimal Design Under Uncertainty; Computer-aided Design Sensitivity Analysis and Optimization of Dynamic Systems; Optimization: The Future of Design, Integration of Optimization with the Design Process; and Optimization in Mechanisms: Past, Present, and Future. It is hoped that the article will prove useful in guiding future efforts in the area of optimal mechanical design.
Skip Nav Destination
Article navigation
Reports
Future Trends in Optimization
G. E. Johnson
G. E. Johnson
School of Engineering and Applied Science, University of Virginia, Charlottesville, VA
Search for other works by this author on:
G. E. Johnson
School of Engineering and Applied Science, University of Virginia, Charlottesville, VA
J. Mech. Des. Oct 1981, 103(4): 677-682 (6 pages)
Published Online: October 1, 1981
Article history
Online:
November 17, 2009
Citation
Johnson, G. E. (October 1, 1981). "Future Trends in Optimization." ASME. J. Mech. Des. October 1981; 103(4): 677–682. https://doi.org/10.1115/1.3254970
Download citation file:
Get Email Alerts
Cited By
A Functional Perspective on the Emergence of Dominant Designs
J. Mech. Des (March 2024)
Toward Artificial Empathy for Human-Centered Design
J. Mech. Des
Related Articles
JCISE Editorial – August 2022
J. Comput. Inf. Sci. Eng (August,2022)
Editorial
J. Mech. Des (July,2006)
Foreword
J. Eng. Gas Turbines Power (April,2013)
Pshenichny’s Linearization Method for Mechanical System Optimization
J. Mech., Trans., and Automation (March,1983)
Related Proceedings Papers
Related Chapters
CAD/CAE Simulation Optimization
Taguchi Methods: Benefits, Impacts, Mathematics, Statistics and Applications
Case Study 2: Queuing Study
Engineering Optimization: Applications, Methods, and Analysis
Develop of New Online Logistics Learning Platform Features - Integration of the Taiwan Train Quality Scorecard and the Kano Model
International Conference on Advanced Computer Theory and Engineering, 4th (ICACTE 2011)