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Title: Selecting a near-optimal design for multiple criteria with improved robustness to different user priorities

Abstract

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 frommore » 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.« less

Authors:
 [1];  [2];  [3];  [4]
  1. Scientific Test and Analysis Techniques Center of Excellence, Dayton, OH (United States)
  2. Univ. of South Florida, Tampa, FL (United States). Dept. of Mathematics and Statistics
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  4. Arizona State Univ., Tempe, AZ (United States). School of Computing, Informatics and Decisions Systems, Engineering Dept. of Industrial Engineering
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
US Department of Homeland Security (DHS); USDOE
OSTI Identifier:
1481982
Report Number(s):
LA-UR-18-20532
Journal ID: ISSN 0748-8017
Grant/Contract Number:  
AC52-06NA25396; PSA‐ASU‐ODEX‐16‐04
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Quality and Reliability Engineering International
Additional Journal Information:
Journal Name: Quality and Reliability Engineering International; Journal ID: ISSN 0748-8017
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Mathematics; DMRCS; D‐optimality; factor correlations; G‐optimality; I‐optimality; Pareto front; power; projections of designs

Citation Formats

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. United States: N. p., 2018. Web. doi:10.1002/qre.2413.
Burke, Sarah E., Lu, Lu, Anderson-Cook, Christine M., & Montgomery, Douglas C. Selecting a near-optimal design for multiple criteria with improved robustness to different user priorities. United States. doi:10.1002/qre.2413.
Burke, Sarah E., Lu, Lu, Anderson-Cook, Christine M., and Montgomery, Douglas C. Mon . "Selecting a near-optimal design for multiple criteria with improved robustness to different user priorities". United States. doi:10.1002/qre.2413.
@article{osti_1481982,
title = {Selecting a near-optimal design for multiple criteria with improved robustness to different user priorities},
author = {Burke, Sarah E. and Lu, Lu and Anderson-Cook, Christine M. and Montgomery, Douglas C.},
abstractNote = {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.},
doi = {10.1002/qre.2413},
journal = {Quality and Reliability Engineering International},
issn = {0748-8017},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on October 29, 2019
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