Optimal design of multivariate acceptance sampling plans by variables

Duarte, Belmiro P. M. and Singh, Satya P. and Moura, Maria J. (2022) Optimal design of multivariate acceptance sampling plans by variables. Journal of Statistical Computation and Simulation. pp. 1-21. ISSN 0094-9655

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Quality-control via acceptance sampling is a technique well established for monitoring the quality of production by lots. The design of acceptance sampling plans for univariate characteristics that follow the Gaussian distribution is reported in various references. Formulations for finding statistically and economically optimal acceptance sampling plans have been consistently proposed. Contrarily, the extension of acceptance sampling to multivariate characteristics has limited applications and the methods available to design plans are still elusive as they involve complex numerical computation procedures. We propose optimization-based formulations to design acceptance sampling plans by variables for multivariate characteristics. First, we consider the independence of characteristics and address plans that satisfy all the controlled quality levels of each one. A Mixed Integer Nonlinear Programming formulation is introduced for such a purpose. Then, we extend the analysis to dependent characteristics and use Surrogate-based optimization to handle the problem. The formulations are demonstrated with simulated scenarios and an industrial case of practical interest. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Article
Uncontrolled Keywords: Acceptance sampling plan; multivariate quality control; optimal design; surrogate-based optimization
Subjects: Mathematics
Chemical Engineering
Chemical Engineering > Biochemical Engineering
Divisions: Department of Chemical Engineering
Department of Mathematics
Depositing User: . LibTrainee 2021
Date Deposited: 20 Jul 2022 11:37
Last Modified: 20 Jul 2022 11:37
URI: http://raiith.iith.ac.in/id/eprint/9817
Publisher URL: http://doi.org/10.1080/00949655.2022.2060223
OA policy: https://v2.sherpa.ac.uk/id/publication/5754
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