Panos Y. Papalambros

Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization

The use of surrogate models or metamodeling has lead to new areas of research in simulation-based design optimization. Metamodeling approaches have advantages over traditional techniques when dealing with the noisy responses and/or high computational cost characteristic of many computer simulations. This paper focuses on a particular algorithm, Efficient Global Optimization (EGO) that uses kriging metamodels. Several infill sampling criteria are reviewed, namely criteria for selecting design points at which the true functions are evaluated. The infill sampling criterion has a strong influence on how efficiently and accurately EGO locates the optimum. Variance-reducing criteria substantially reduce the RMS error of the resulting metamodels, while other criteria influence how locally or globally EGO searches. Criteria that place more emphasis on global searching require more iterations to locate optima and do so less accurately than criteria emphasizing local search.

Analytical Target Cascading in Automotive Vehicle Design

Target cascading in product development is a systematic effort to propagate the desired top-level system design targets to appropriate specifications for subsystems and components in a consistent and efficient manner. If analysis models are available to represent the consequences of the relevant design decisions, analytical target cascading can he formalized as a hierarchical multilevel optimization problem. The article demonstrates this complex modeling and solution process in the chassis design of a sport-utility vehicle. Ride quality and handling targets are cascaded down to systems and subsystems utilizing suspension, tire, and spring analysis models. Potential incompatibilities among targets and constraints Throughout the entire system can he uncovered and the trade-offs involved in achieving system targets under different design scenarios can he quantified.

Convergence properties of analytical target cascading

Analytical target cascading (ATC) is a relatively new methodology for the design of engineering systems. ATC deals with the issue of propagating desirable top level product design specifications (or targets) to appropriate targets at lower levels in a consistent and ecient manner. Most existing problem formulations for multilevel design often exhibit convergence diculties. In this article, it is proved that under convexity assumptions the ATC process converges to the optimal solution of the original design target problem.

A NOTE ON WEIGHTED CRITERIA METHODS FOR COMPROMISE SOLUTIONS IN MULTI-OBJECTIVE OPTIMIZATION

A common multi-objective optimization approach forms the objective function from linearly weighted criteria. It is known that the method can fail to capture Pareto optimal points in a non-convex attainable region. This note considers generalized weighted criteria methods that retain the advantages of the linear method without suffering from this limitation. Compromise programming and a new method with exponentially weighted criteria are evaluated. Demonstration on design problems is included.

Multicriteria Optimization in Product Platform Design

A product platform is a set of common components, modules or parts from which a stream of derivative products can be created. Product platform design requires selection of the shared parts and assessment of the potential sacrifices in individual product performance that result from parts sharing. A multicriteria optimization problem can be formulated to study such decisions in a quantitative manner at the product performance level. Studying the Pareto sets that correspond to various derivative products leads to a systematic methodology for design decision making. Design of a nail gun platform is used to illustrate the concepts presented.

Combined optimisation of design and power management of the hydraulic hybrid propulsion system for the 6 × 6 medium truck

Hybrid propulsion systems are one of the critical technologies on the roadmap to future ultra-efficient trucks. While there is a significant body of work related to hybrid passenger cars and light ...

Balancing Marketing and Manufacturing Objectives in Product Line Design

The product development process involves communication and compromise among interacting and often competing objectives from marketing, design, and manufacturing perspectives. Methods for negotiating these perspectives play an important role in the process. For example, design for manufacturing (DFM) analyses aim to incorporate manufacturing requirements into product design decision making to reduce product complexity and cost, which generally increases profitability. However, when design characteristics have market consequences, it is important to quantify explicitly the tradeoffs between the reduced cost and reduced revenue resulting from designs that are less expensive to manufacture but also less desirable in the marketplace. in this article we leverage existing models for coordinating marketing and design perspectives by incorporating quantitative models of manufacturing investment and production allocation. The resulting methodology allows a quantitative assessment of tradeoffs among product functionality, market performance, and manufacturing costs to achieve product line solutions with optimal profitability.

Target cascading in vehicle redesign: a class VI truck study

The analytical target cascading process is applied to the redesign of a U.S. class VI truck. Necessary simulation and analysis models for predicting vehicle dynamics, powertrain, and suspension behaviour are developed. Vehicle design targets that include improved fuel economy, ride quality, driveability, and performance metrics are translated into system design specifications, and a consistent final design is obtained. Trade-offs between conflicting targets are identified. The study illustrates how the analytical target cascading process can reduce vehicle design cycle time while ensuring physical prototype matching, and how costly design iterations late in the development process can be avoided.

On the coupling between the plant and controller optimization problems

Examines plant and controller optimization problems. One can solve these problems sequentially, iteratively, using a nested (or bi-level) strategy, or simultaneously. Unlike the nested and simultaneous strategies, the sequential and iterative strategies fail to guarantee system-level optimality. This is because the plant and controller optimization problems are coupled. This coupling is introduced using a simple experiment. To prove it theoretically, the necessary conditions for combined plant and controller optimality are derived. These combined optimality conditions differ from the individual sets of necessary conditions for plant and controller optimality by a coupling term that reflects the plant designs influence on the plant dynamics and control input constraints.

Architectural layout design optimization

This article presents an optimization model of the quantifiable aspects of architectural floorplan layout design, and a companion article presents a method for integrating mathematical optimization and subjective decision making during conceptual design. The model presented here offers a new approach to floorplan layout optimization that takes advantage of the efficiency of gradient-based algorithms, where appropriate, and uses evolutionary algorithms to make discrete decisions and do global search. Automated optimization results are comparable to other methods in this research area, and the new formulation makes it possible to integrate the power of human decision-making into the process.