Selecting a near-optimal design for multiple criteria with improved robustness to different user priorities
- Scientific Test and Analysis Techniques Center of Excellence, Dayton, OH (United States)
- Univ. of South Florida, Tampa, FL (United States). Dept. of Mathematics and Statistics
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Arizona State Univ., Tempe, AZ (United States). School of Computing, Informatics and Decisions Systems, Engineering Dept. of Industrial Engineering
In a decision-making process, relying on only one objective can often lead to oversimplified decisions that ignore important considerations. Incorporating multiple, and likely competing, objectives is critical for balancing trade-offs on different aspects of performance. When multiple objectives are considered, it is often hard to make a precise decision on how to weight the different objectives when combining their performance for ranking and selecting designs. We show that there are situations when selecting a design with near-optimality for a broad range of weight combinations of the criteria is a better test selection strategy compared with choosing a design that is strictly optimal under very restricted conditions. Here, we propose a new design selection strategy that identifies several top-ranked solutions across broad weight combinations using layered Pareto fronts and then selects the final design that offers the best robustness to different user priorities. This method involves identifying multiple leading solutions based on the primary objectives and comparing the alternatives using secondary objectives to make the final decision. We focus on the selection of screening designs because they are widely used both in industrial research, development, and operational testing. The method is illustrated with an example of selecting a single design from a catalog of designs of a fixed size. However, the method can be adapted to more general designed experiment selection problems that involve searching through a large design space.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- US Department of Homeland Security (DHS); USDOE
- Grant/Contract Number:
- AC52-06NA25396; PSA‐ASU‐ODEX‐16‐04
- OSTI ID:
- 1481982
- Report Number(s):
- LA-UR-18-20532
- Journal Information:
- Quality and Reliability Engineering International, Vol. 35, Issue 3; ISSN 0748-8017
- Publisher:
- WileyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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