Probabilistic Analytical Target Cascading: A Moment Matching Formulation for Multilevel Optimization Under Uncertainty

2006 ◽  
Vol 128 (4) ◽  
pp. 991 ◽  
Author(s):  
Huibin Liu ◽  
Wei Chen ◽  
Michael Kokkolaras ◽  
Panos Y. Papalambros ◽  
Harrison M. Kim
Keyword(s):  
Optimization Under Uncertainty ◽  
Moment Matching ◽  
Analytical Target Cascading ◽  
Multilevel Optimization ◽  
Target Cascading ◽  
Probabilistic Analytical Target Cascading

Probabilistic Analytical Target Cascading: A Moment Matching Formulation for Multilevel Optimization Under Uncertainty

2005 ◽  
Author(s):  
Huibin Liu ◽  
Wei Chen ◽  
Michael Kokkolaras ◽  
Panos Y. Papalambros ◽  
Harrison M. Kim
Keyword(s):  
Probabilistic Approach ◽  
Optimization Under Uncertainty ◽  
Analytical Target Cascading ◽  
Multilevel Optimization ◽  
Coordination Strategies ◽  
Simulation Based ◽  
Target Cascading ◽  
Design Consistency ◽  
System Design Optimization ◽  
Probabilistic Analytical Target Cascading

Analytical target cascading (ATC) is a methodology for hierarchical multilevel system design optimization. In previous work, the deterministic ATC formulation was extended to account for uncertainties using a probabilistic approach. Random quantities were represented by their expected values, which were required to match among subproblems to ensure design consistency. In this work, the probabilistic formulation is augmented to allow introduction and matching of additional probabilistic characteristics. Applying robust design principles, a particular probabilistic analytic target cascading (PATC) formulation is proposed by matching the first two moments of random quantities. Several implementation issues are addressed, including representation of probabilistic design targets, matching interrelated responses and linking variables under uncertainty, and coordination strategies for multilevel optimization. Analytical and simulation-based optimal design examples are used to illustrate the new PATC formulation. Design consistency is achieved by matching the first two moments of interrelated responses and linking variables. The effectiveness of the approach is demonstrated by comparing PATC results to those obtained using a probabilistic all-in-one (PAIO) formulation.

Analytical Target Cascading in Automotive Vehicle Design

2003 ◽  
Vol 125 (3) ◽  
pp. 481-489 ◽  
Author(s):  
Hyung Min Kim ◽  
D. Geoff Rideout ◽  
Panos Y. Papalambros ◽  
Jeffrey L. Stein
Keyword(s):  
Solution Process ◽  
Ride Quality ◽  
Analytical Target Cascading ◽  
Multilevel Optimization ◽  
Efficient Manner ◽  
Trade Offs ◽  
Systematic Effort ◽  
Target Cascading ◽  
Analysis Models ◽  
Hierarchical Multilevel Optimization

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 be 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 be uncovered and the trade-offs involved in achieving system targets under different design scenarios can be quantified.

Robust Multiscale Design Using PATC and WSRSM

2010 ◽  
Vol 148-149 ◽  
pp. 1075-1078
Author(s):  
Fen Fen Xiong ◽  
Gao Rong Sun ◽  
Liang Yu Zhao
Keyword(s):  
Design Problem ◽  
Design System ◽  
Analytical Target Cascading ◽  
Product System ◽  
Stochastic Response ◽  
Multilevel Structure ◽  
Surface Method ◽  
Target Cascading ◽  
Probabilistic Analytical Target Cascading ◽  
Bracket Design

Multiscale design dealing with 2-scale material and product system is implemented by employing probabilistic analytical target cascading (PATC) and weighted stochastic response surface method (WSRSM) in this paper. PATC allows design autonomy at each scale subsystem by formulating the multiscale design system as a multilevel structure. WSRSM ensures uncertainties to be propagated within and across each scale accurately and efficiently. Comparative study on a multiscale bracket design problem shows that the results obtained by our strategy are very close to the reference values. It is demonstrated that PATC and WSRSM are highly effective and applicable on multiscale design.

Analytical Target Cascading in Automotive Vehicle Design

2001 ◽  
Author(s):  
Hyung Min Kim ◽  
D. Geoff Rideout ◽  
Panos Y. Papalambros ◽  
Jeffrey L. Stein
Keyword(s):  
Solution Process ◽  
Ride Quality ◽  
Analytical Target Cascading ◽  
Multilevel Optimization ◽  
Efficient Manner ◽  
Trade Offs ◽  
Systematic Effort ◽  
Target Cascading ◽  
Analysis Models ◽  
Hierarchical Multilevel Optimization

Abstract 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 relevant design decisions, analytical target cascading can be 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 be uncovered and the trade-offs involved in achieving system targets under different design scenarios can be quantified.

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