Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 59, Issue 15, Pages 4649-4669Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2020.1769873
Keywords
Response surface methodology; Bayesian hierarchical modelling; model selection and estimation; SUR models; process optimisation
Categories
Funding
- National Natural Science Foundation of China [71931006, 71702072, 71871119]
- Fundamental Research Funds for the Central Universities [NR2019002]
- Natural Science Foundation for Jiangsu Institutions [BK20170810]
- National Research Foundation of Korea [2018K2A9A2A06019662]
- China Postdoctoral Science Foundation [2019T120429]
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This paper introduces a Bayesian hierarchical modelling approach and SUR model for process optimisation, aiming to improve efficiency through model selection and estimation. Additionally, a two-stage optimisation strategy considering practitioners' preference information is proposed to find the best settings and compare them with an ideal solution method.
Many industrial process optimisation methods rely on empirical models that relate output responses to a set of design variables. One of the most crucial problems in process optimisation is how to efficiently implement model selection and model estimation. This paper presents a Bayesian hierarchical modelling approach to process optimisation based on the seemingly unrelated regression (SUR) models. This approach can estimate a set of predictors to be included in a model based on a Bayesian hierarchical procedure (i.e. model selection) and then give model prediction based on a Bayesian SUR model (i.e. model estimation). Meanwhile, a two-stage optimisation strategy considering practitioners' preference information is proposed in process optimisation, which initially finds a set of non-dominated input settings and then determines the best one based on the similarity to an ideal solution method. The performance and effectiveness of the proposed method are illustrated with both simulation studies and a case study. The comparison results demonstrate that the proposed method can be a good alternative to existing process optimisation methods.
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