Determination of ranged sets of design specifications by incorporating design-space heterogeneity

Liu, Huibin; Chen, Wei; Scott, Michael J.; Qureshi, Khurshid

doi:10.1080/03052150802378558

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…In addition to decomposition, classification decision boundaries can be chosen such as intervals that eliminate the dependency between groups. A particularly interesting approach that would be a nice complement to this research for determining intervals based upon subsystem achievability is developed in (Liu et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Bayesian Network Classifiers for Set-Based Collaborative Design

Shahan

Seepersad

2010

Volume 1: 36th Design Automation Conference, Parts a and B

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Complex design problems are typically decomposed into smaller design problems that are solved by domain-specific experts who must then coordinate their solutions into a satisfactory system-wide solution. In set-based collaborative design, collaborating engineers coordinate themselves by communicating multiple design alternatives at each step of the design process. The goal in set-based collaborative design is to spend additional resources exploring multiple options in the early stages of the design process, in exchange for less iteration in the latter stages, when iterative rework tends to be most expensive. Several methods have been proposed for representing sets of designs, including intervals, surrogate models, fuzzy membership functions, and probability distributions. In this paper, we introduce the use of Bayesian networks for capturing sets of promising designs, thereby classifying the design space into satisfactory and unsatisfactory regions. The method is compared to intervals in terms of its capacity to accurately classify satisfactory design regions as a function of the number of available data points. A simplified, multilevel design problem for an unmanned aerial vehicle is presented as the motivating example.

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Section: Discussionmentioning

confidence: 99%

Bayesian Network Classifiers for Set-Based Collaborative Design

Shahan

Seepersad

2010

Volume 1: 36th Design Automation Conference, Parts a and B

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“…Other approaches include the use of physical programming to differentiate solutions based on their quality [37,38], where all designs within the same class are viewed as having an equivalent performance. Other work has explored the use of target sets to decompose the design space and identify optimal solutions [39]. Further opportunities can be found in robust design, where research has characterized the effect of design variable variations on performance [40] while applying regionalized sensitivity analysis [41] and internal-reduction measures [42].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Effectiveness of Using Graveyard Data When Generating Design Alternatives

Foster¹,

Ferguson

2013

Journal of Computing and Information Science in Engineering

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The objective of this paper is to demonstrate that unique alternative designs can be efficiently found by searching the discarded data (or graveyard) from a multiobjective genetic algorithm (MOGA). Motivation for using graveyard data to generate design alternatives arises from the computational cost associated with real-time design space exploration of multiobjective optimization problems. The effectiveness of this approach is explored by comparing (1) the uniqueness of alternatives found using graveyard data and those generated using an optimization-based search, and (2) how alternative generation near the Pareto frontier is impacted. Two multiobjective case study problems are introduced-a two bar truss and an I-beam design optimization. Results from these studies indicate that using graveyard data allows for the discovery of alternative designs that are at least 70% as unique as alternatives found using an optimization-based alternative identification approach, while saving a significant number of functional evaluations. Additionally, graveyard data are shown to be better suited for alternative generation near the Pareto frontier than standard sampling techniques. Finally, areas of future work are also discussed.

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“…Robust design techniques have been used for generating ranged sets or intervals of design specifications that can be shared with collaborating designers [9][10][11]. In recent work, Liu and coauthors [12] established a set theory-based quantization algorithm for dividing a design space into regions and a flexibility metric, based on aggregated achievability functions, for selecting the most achievable region or ranged set of design specifications. Like recent constraint-based approaches to set-based design [13], these interval-based approaches tend to provide relatively rigid representations of satisfactory solutions that do not capture arbitrarily shaped and potentially disconnected regions of the design space.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Networks for Set-Based Collaborative Design

Shahan

Seepersad

2009

Volume 5: 35th Design Automation Conference, Parts a and B

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A set-based approach to collaborative design is presented, in which Bayesian networks are used to represent promising regions of the design space.In collaborative design exploration, complex multilevel design problems are often decomposed into distributed subproblems that are linked by shared or coupled parameters. Collaborating designers often prefer conflicting values for these coupled parameters, resulting in incompatibilities that require substantial iteration to resolve, extending the design process lead time without guarantee of achieving a good design. In the proposed approach to collaborative design, each designer builds a locally developed Bayesian network that represents regions of interest in his design space. Then, these local networks are shared and combined with those of collaborating designers to promote more efficient local design space search that takes into account the interests of one's collaborators. The proposed method has the potential to capture a designer's preferences for arbitrarily shaped and potentially disconnected regions of the design space in order to identify compatible or conflicting preferences between collaborators and to facilitate a compromise if necessary. It also sets the stage for a flexible and concurrent design process with varying degrees of designer involvement that can support different designer strategies such as hill-climbing or region identification. The potential benefits are the capture of expert knowledge for future use as well as conflict identification and resolution. This paper presents an overview of the proposed method as well as an example implementation for the design of an unmanned aerial vehicle.

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Determination of ranged sets of design specifications by incorporating design-space heterogeneity

Cited by 8 publications

References 25 publications

Bayesian Network Classifiers for Set-Based Collaborative Design

Bayesian Network Classifiers for Set-Based Collaborative Design

Exploring the Effectiveness of Using Graveyard Data When Generating Design Alternatives

Bayesian Networks for Set-Based Collaborative Design

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