Selecting a Near-optimal Design for Multiple Criteria with Improved Robustness to Different User Priorities
DMRCS, D-optimality, factor correlations, G-optimality, I-optimality, Pareto front, power, projections of designs
Digital Object Identifier (DOI)
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. 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.
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Citation / Publisher Attribution
Quality and Reliability Engineering International, v. 35, issue 3, p. 788-814
Scholar Commons Citation
Burke, Sarah E.; Lu, Lu; Anderson-Cook, Christine M.; and Montgomery, Douglas C., "Selecting a Near-optimal Design for Multiple Criteria with Improved Robustness to Different User Priorities" (2019). Mathematics and Statistics Faculty Publications. 144.