Generalized mathematical models in design optimization

The theory of optimality conditions of extremal problems can be extended to problems continuously deformed by an input vector. The connection between the sensitivity, well-posedness, stability and approximation of optimization problems is steadily emerging. The authors believe that the important realization here is that the underlying basis of all such work is still the study of point-to-set maps and of small perturbations, yet what has been identified previously as being just related to solution procedures is now being extended to study modeling itself in its own right. Many important studies related to the theoretical issues of parametric programming and large deformation in nonlinear programming have been reported in the last few years, and the challenge now seems to be in devising effective computational tools for solving these generalized design optimization models

Accuracy considerations in approximate reanalysis of structures

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76782/1/AIAA-2000-4751-508.pd

Special issue on commercial design optimization software: an introductory note

Twenty years later I have come to understand the true meaning of Jim Siddall’s comment. Having worked with several colleagues in industry across the world and having followed their work quite closely I came to a couple of conclusions: The complexity of the optimal design problems that our industry colleagues address successfully far exceeds what many of us academics suspect or even dare undertake. Furthermore, the true heroes of the indisputable growth of design optimization use in industry (or the academy for that matter) are the “commercial” software vendors–the companies and the people who have tried to put together software packages that are robust, friendly and accessible to users that may lack the sophistication or the time to use the immediate products of the academic endeavor. To be fair, these commercial software entities would not have been possible without the intellectual fruits of the academic research. The point however is not to minimize the latter’s importance; rather, it is to recognize that we should applaud all such “commercial” efforts, filter the hype of the marketplace, and support their collective use by students and colleagues in academia and industry alike. For example, thanks to the Excel Solver I have been able to do the unimaginable (to me anyway): have second-year college sophomores use design optimization intelligently in their project work.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41907/1/158-23-2-95_20230095.pd

Application of a product platform design process to automotive powertrains

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77031/1/AIAA-2000-4849-139.pd

Sequentially decomposed programming

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76276/1/AIAA-1996-4008-190.pd

A modification to Jones' global optimization algorithm for fast local convergence

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77127/1/AIAA-1998-4751-286.pd

Computation of Khun-Tucker triples in optimum design problems in the presence of parametric singularities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76242/1/AIAA-1994-4416-414.pd

Global Optimization of Problems with Disconnected Feasible Regions via Surrogate Modeling

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77089/1/AIAA-2002-5573-619.pd

Impact of Uncertainty Quantification on Design Decisions for a Hydraulic-Hybrid Powertrain Engine

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76707/1/AIAA-2006-2001-814.pd

Metamodeling sampling criteria in a global optimization framework

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76903/1/AIAA-2000-4921-115.pd