Solving complex design problems through multiobjective optimisation taking into account judgements of users

2014 ◽  
Vol 25 (3) ◽  
pp. 223-239 ◽  
Author(s):  
Céline Villa ◽  
Raphael Labayrade
Keyword(s):  
Multiobjective Optimisation ◽  
Design Problems ◽  
Complex Design

Multi-Objective Optimization of an Autonomous Underwater Vehicle

2009 ◽  
Vol 43 (2) ◽  
pp. 48-60 ◽  
Author(s):  
M. Martz ◽  
W. L. Neu
Keyword(s):  
Design Space ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Design Problems ◽  
Single Measure ◽  
Design Variables ◽  
Complex Design ◽  
Final Design ◽  
Undersea Vehicle ◽  
Hierarchy Process

AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.

A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems

1995 ◽  
Author(s):  
Stephen S. Altus ◽  
Ilan M. Kroo ◽  
Peter J. Gage
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Heuristic Method ◽  
Merit Function ◽  
Multidisciplinary Design ◽  
Design Problems ◽  
Design Studies ◽  
Complex Design ◽  
Complex Engineering

Abstract Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.

Understand Complex Design Problems Using Systems Thinking

2011 ◽  
pp. 379-393 ◽  
Author(s):  
Tao Huang ◽  
Eric E. Anderson
Keyword(s):  
Product Design ◽  
Systems Theory ◽  
Design Research ◽  
Current System ◽  
Design Problems ◽  
Evolving Systems ◽  
Complex Design ◽  
Research Problems ◽  
Dynamics Models ◽  
New System

This chapter provides a brief overview of systems theory and suggests that product designers could use systems theory and systems dynamics models to improve our understanding of complex Product Design research problems, to anticipate how and where changes in these dynamically evolving systems might occur and how they might interact with the current system to produce a new system with new behaviors, and to identify leverage points within the system where potential policy or design process changes might be introduced to produce effective solutions to these problems with minimum policy resistance. By investigating the current and future trends of the application of systems theory in Product Design research, this chapter invites multidisciplinary discussions of these topics.

A Proposal of Classification and Guideline of Selection for Design Modeling Methods

2007 ◽  
Author(s):  
Takashi Asanuma ◽  
Jumpei Kawashima ◽  
Yoshiki Ujiie ◽  
Yoshiyuki Matsuoka
Keyword(s):  
Design Process ◽  
Design Solution ◽  
Human Beings ◽  
Design Problems ◽  
Modeling Methods ◽  
Complex Design ◽  
The Social ◽  
Selection For ◽  
Design Objects ◽  
Exact Design

In recent years the demands of users and the social problems have been diverse. In design, the diverse demands of users and problems of the society have created increasingly complex design problems. Therefore, it is important to understand values and images of the design objects and analyze the relation among design objects, human beings and its environment to respond to the complicated design problems. A number of design modeling methods that realize above points have been proposed. Consequently, it is necessary for designers and engineers to derivate the exact design solution that responds to the complicated design problems. However, the framework of design modeling methods in design has not been established. Moreover, most of the current studies on the methods only respond to the problems in each aspect of design [1]. Therefore, designers and engineers apply the design modeling methods in each design process based on their knowledge and experiences. The guideline of selection for the application of design modeling methods has not been shown. Consequently, the guideline for selecting the design modeling methods is needed for designers and engineers to apply the methods appropriately in design.

Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments

2006 ◽  
Author(s):  
Jun Liu ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Robust Design ◽  
Prediction Interval ◽  
Computer Experiments ◽  
Design Problems ◽  
Deterministic Optimization ◽  
Automotive Engine ◽  
Design Objective ◽  
Design Variables ◽  
Complex Design ◽  
The Impact

The use of metamodels in simulation-based robust design introduces a new source of uncertainty that we term model interpolation uncertainty. Most existing approaches for treating interpolation uncertainty in computer experiments have been developed for deterministic optimization and are not applicable to design under uncertainty. With the randomness present in noise and/or design variables that propagates through the metamodel, the effects of model interpolation uncertainty are not nearly as transparent as in deterministic optimization. In this work, a methodology is developed within a Bayesian framework for quantifying the impact of interpolation uncertainty on robust design objective. By viewing the true response surface as a realization of a random process, as is common in kriging and other Bayesian analyses of computer experiments, we derive a closed-form analytical expression for a Bayesian prediction interval on the robust design objective function. This provides a simple, intuitively appealing tool for distinguishing the best design alternative and conducting more efficient computer experiments. Even though our proposed methodology is illustrated with a simple container design and an automotive engine piston design example here, the developed analytical approach is the most useful when applied to high-dimensional complex design problems in a similar manner.

Tree-Based Dependency Analysis in Decomposition and Re-decomposition of Complex Design Problems

2005 ◽  
Vol 127 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Li Chen ◽  
Zhendong Ding ◽  
Simon Li
Keyword(s):  
Formal Method ◽  
Cost Effective ◽  
Tree Structure ◽  
Solution Synthesis ◽  
Dependency Analysis ◽  
Design Problems ◽  
Complex Design ◽  
Two Phases ◽  
Binary Tree Structure ◽  
Matrix Partitioning

We have developed a formal method for decomposition of complex design problems in two phases: dependency analysis and matrix partitioning. Of the most distinct characteristic in this method is the support of cost-effective re-decomposition (as is often required in decomposition solution synthesis), where dependency analysis serves as a platform for the enabling of re-decomposition. Yet, this requires that the result of the dependency analysis be robust and thus reusable for re-decomposition. In this paper, after revealing the deficiency in the current practice of dependency analysis, we present an enhanced dependency analysis method that is built on ordinary tree structure (instead of binary tree structure). This new approach, which is more systematic, ensures robust dependency analysis, whose result is insensitive to the arrangement of a tree structure in tree-based dependency analysis. A complete set of tree-based algorithms is also provided, along with their applications to two design examples

Employing Multidimensional Data Visualization Tools to Assess the Impact of Constraint Uncertainties on Complex Design Problems

2017 ◽  
Author(s):  
Gary M. Stump ◽  
Simon W. Miller ◽  
Michael A. Yukish ◽  
Christopher M. Farrell
Keyword(s):  
Data Visualization ◽  
Decision Makers ◽  
Multidimensional Data ◽  
Design Problems ◽  
Design Constraints ◽  
Performance Space ◽  
Multidimensional Data Visualization ◽  
Complex Design ◽  
Visualization Tools ◽  
The Impact

A potential source of uncertainty within multi-objective design problems can be the exact value of the underlying design constraints. This uncertainty will affect the resulting performance of the selected system commensurate with the level of risk that decision-makers are willing to accept. This research focuses on developing visualization tools that allow decision-makers to specify uncertainty distributions on design constraints and to visualize their effects in the performance space using multidimensional data visualization methods to solve problems with high orders of computational complexity. These visual tools will be demonstrated using an example portfolio design scenario in which the goal of the design problem is to maximize the performance of a portfolio with an uncertain budget constraint.

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