國立虎尾科技大學 |

Design as a Sequential Decision Process : = A Method for Reducing Set Space Using Models to Bound Objectives.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Design as a Sequential Decision Process :/
其他題名:	A Method for Reducing Set Space Using Models to Bound Objectives.
作者:	Miller, Simon W.
面頁冊數:	1 online resource (161 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Mechanical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369991994

Design as a Sequential Decision Process : = A Method for Reducing Set Space Using Models to Bound Objectives.
Miller, Simon W.

Design as a Sequential Decision Process :A Method for Reducing Set Space Using Models to Bound Objectives. - 1 online resource (161 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Complex engineered systems rely heavily on simulation models for design. Designing such systems typically involves a sequential decision process that increases the modeling and engineering analysis detail while simultaneously decreasing the space of alternatives considered. This space of alternatives is called the tradespace, and in a decision theoretic framework, low-fidelity models help decision-makers identify regions of interest in the tradespace and cull others prior to constructing more computationally-expensive higher fidelity models to further discriminate alternatives in support of a model-based systems engineering process.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369991994Subjects--Topical Terms:

557493
Mechanical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Design as a Sequential Decision Process : = A Method for Reducing Set Space Using Models to Bound Objectives.
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300 $a 1 online resource (161 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
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502 $a Thesis (Ph.D.) $c The Pennsylvania State University $d 2017.
504 $a Includes bibliographical references
520 $a Complex engineered systems rely heavily on simulation models for design. Designing such systems typically involves a sequential decision process that increases the modeling and engineering analysis detail while simultaneously decreasing the space of alternatives considered. This space of alternatives is called the tradespace, and in a decision theoretic framework, low-fidelity models help decision-makers identify regions of interest in the tradespace and cull others prior to constructing more computationally-expensive higher fidelity models to further discriminate alternatives in support of a model-based systems engineering process.
520 $a Previous work has shown that multi-fidelity modeling can aid in rapid optimization of the design space when higher fidelity models are coupled with lower fidelity models, i.e., it is assumed that both models are available, and the two are used as a computational "trick" to speedup optimization routines. This dissertation introduces and motivates the sequential process of design through a formal model. The formal model seeks to answer (1) what structure models of varying fidelities should have relative to one another, (2) a linkage between decisions made from each modeling effort, and (3) a method for selecting a sequence of computational models and analyses when considering design as a sequential decision process. The method presented herein demonstrates design as a sequence of finite decision epochs through a tradespace defined by the extent of the set of designs under consideration, modeling characteristics, and the level of analytic fidelity subjected to each design. This work introduces a method for constructing cost-optimal sequences of models of increasing fidelity by reducing the design set size under the guarantee that the optimal solution remains in the consideration set at each epoch.
520 $a A summary of relevant literature in design sciences, social sciences, and engineering optimization communities is given to scope the research. The formal model is presented with its assumptions, definitions, and applications as well as a method for applying the model to an engineering system. The method is applied to several examples including 1D and 2D finite element analysis and a bi-level optimization problem with a combinatorial kernel based on a 2D bin packing problem. It is demonstrated that by treating design as a sequential decision process, significant savings (e.g., time, money) can be obtained.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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