INFORMS Journal on Data Science

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Ano-SuPs: Multisize Anomaly Detection for Manufactured Products by Identifying Suspected Patches via Vision Transformer
- Hao Xu
  Hao Xu
  [email protected]
  Smart Manufacturing Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong 511453, China
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- Juan Du
  Corresponding Author
  Juan Du
  [email protected]
  https://orcid.org/0000-0002-6018-2972
  Smart Manufacturing Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong 511453, China; and Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong Special Administrative Region, China
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- Andi Wang
  Andi Wang
  [email protected]
  https://orcid.org/0000-0003-4925-1962
  Department of Industrial and Systems Engineering, College of Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706
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- Yingcong Chen
  Yingcong Chen
  [email protected]
  AI Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong 511453, China; and Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong Special Administrative Region, China
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Published Online:June 19, 2026
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Abstract
Image-based systems have gained popularity owing to their capacity to provide rich manufacturing status information, low implementation costs, and high acquisition rates. However, the complexity of the image background and various anomaly patterns pose ...
Sequential Change Point Detection in Dynamic Non-Stationary Manufacturing Processes
- Yuhan Tian
  Yuhan Tian
  [email protected]
  Department of Statistics, University of Florida, Gainesville, Florida 32611
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- Abolfazl Safikhani
  Corresponding Author
  Abolfazl Safikhani
  [email protected]
  https://orcid.org/0000-0001-8678-1247
  Department of Statistics, George Mason University, Fairfax, Virginia 22030
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- Kamran Paynabar
  Kamran Paynabar
  [email protected]
  https://orcid.org/0000-0002-6906-3611
  ISYE, Georgia Institute of Technology, Atlanta, Georgia 30332
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Published Online:June 12, 2026
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Abstract
Sequential monitoring of multivariate time series to detect sudden changes in the data-generating process is a fundamental problem in statistics and signal processing. Most existing detection algorithms assume (a) no cross-correlations between time series ...
ℓ₁-Norm Regularized ℓ₁-Norm Best-Fit Lines
- Xiao Ling
  Corresponding Author
  Xiao Ling
  [email protected]
  https://orcid.org/0000-0002-7134-3743
  Department of Mathematics, Auburn University at Montgomery, Montgomery, Alabama 36117
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- J. Paul Brooks
  J. Paul Brooks
  [email protected]
  https://orcid.org/0000-0003-0423-8422
  Department of Information Systems, Virginia Commonwealth University, Richmond, Virginia 23284
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Published Online:June 4, 2026
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Abstract
We propose an optimization framework for estimating a sparse outlier-insensitive one-dimensional subspace. Our objective is to minimize both the representation error and a penalty on the loadings using an ℓ1-norm criterion for each. To our knowledge, ...
Robust and Interpretable Policy Learning for Manufacturing Process Parameters
- Yu-Hsin Hung
  Yu-Hsin Hung
  [email protected]
  https://orcid.org/0009-0006-8842-5071
  Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
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- Bo-Ru Chen
  Bo-Ru Chen
  [email protected]
  Department of Information Management, National Taiwan University, Taipei 10617, Taiwan
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- Chia-Yen Lee
  Corresponding Author
  Chia-Yen Lee
  [email protected]
  https://orcid.org/0000-0002-2928-3337
  Department of Information Management, National Taiwan University, Taipei 10617, Taiwan
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Published Online:May 5, 2026
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Abstract
In manufacturing systems, process parameters (controllable factors) strongly influence quality outcomes, yet substantial instance-level heterogeneity—arising from environmental conditions, material variability, and machine states—often leads to context-...
A Nested Design and Analysis of Computer Experiments Approach to Enable Design for Controllability of a System of Electric Vehicle Charging Stations
- Ukesh Chawal
  Ukesh Chawal
  [email protected]
  https://orcid.org/0000-0002-0858-9492
  Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, Arlington, Texas 76019; and Boeing Technology Innovation, The Boeing Company, Dallas, Texas 75261
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- Ying Chen
  Corresponding Author
  Ying Chen
  [email protected]
  https://orcid.org/0000-0002-0366-131X
  School of Management, Harbin Institute of Technology, Heilongjiang 150001, China
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- Victoria C. P. Chen
  Victoria C. P. Chen
  [email protected]
  Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, Arlington, Texas 76019
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- Jay M. Rosenberger
  Jay M. Rosenberger
  [email protected]
  Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, Arlington, Texas 76019
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- Wei-Jen Lee
  Wei-Jen Lee
  [email protected]
  Department of Electrical Engineering, The University of Texas at Arlington, Arlington, Texas 76019
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Published Online:May 5, 2026
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Abstract
This paper addresses the optimization of renewable energy–equipped electric vehicle (EV) charging stations in the Dallas–Fort Worth area. We propose a nested design and analysis of computer experiments (DACE) framework that integrates data science and ...

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