| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000434428 |
| 005 | | 20200226150737 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781687927538 |
| 035 | |
▼a (MiAaPQ)AAI27536091 |
| 035 | |
▼a (MiAaPQ)umichrackham002182 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621 |
| 100 | 1 |
▼a Claus, Lauren Rose. |
| 245 | 10 |
▼a Design Space Covering for Uncertainty: Exploration of a New Methodology for Decision Making in Early Stage Design. |
| 260 | |
▼a [S.l.]:
▼b University of Michigan.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 90 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Collette, Matthew David. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Michigan, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 506 | |
▼a This item must not be added to any third party search indexes. |
| 520 | |
▼a Decisions made in early-stage design are of vital importance as they significantly impact the quality of the final design. Despite recent developments in design theory for early-stage design, designers of large complex systems still lack sufficient tools to make robust and reliable preliminary design decisions that do not have a lasting negative impact on the final design. Much of the struggle stems from uncertainty in early-stage design due to loosely defined problems and unknown parameters. Existing methods to handle this uncertainty in point-based design provide feasible, but often suboptimal, solutions that cover the range of uncertainty. Robust Optimization and Reliability Based Design Optimization are examples of point-based design methods that handle uncertainty. To maintain feasibility over the range of uncertainty, these methods accept suboptimal designs resulting in a design margin. In set-based design, design decisions are delayed preventing suboptimal final designs but at the expense of computational efficiency. This work proposes a method that evaluates a compromise between these two methodologies by evaluating the trade off of the induced regret and computational cost of keeping a larger design space. The design space covering for uncertainty (DSC-U) problem defines the metrics regret, which measures suboptimality, and space remaining, which quantifies the design space size after it is reduced. Solution methods for the DSC-U problem explore the trade space between these two metrics. When there is uncertainty in a problem, and the design space is reduced, there is the possibility that the optimal solution for the realized values of the uncertainty parameters has been eliminated |
| 590 | |
▼a School code: 0127. |
| 650 | 4 |
▼a Naval engineering. |
| 690 | |
▼a 0468 |
| 710 | 20 |
▼a University of Michigan.
▼b Naval Architecture & Marine Engineering. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0127 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15494184
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |