期刊
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
卷 59, 期 17, 页码 8281-8294出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.iecr.9b06907
关键词
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资金
- Academy of Finland project SINGPRO [313466, 313469]
- CSC Finland [2000935]
- Academy of Finland (AKA) [313469, 313469] Funding Source: Academy of Finland (AKA)
This paper proposes an efficient decision support tool for the optimal production scheduling of a variety of paper grades in a paper machine. The tool is based on a continuous-time scheduling model and generalized disjunctive programming. As the full-space scheduling model corresponds to a large-scale mixed integer linear programming model, we apply data analytics techniques to reduce the size of the decision space, which has a profound impact on the computational efficiency of the model and enables us to support the solution of large-scale problems. The data-driven model is based on an automated method of identifying the forbidden and recommended paper grade sequences, as well as the changeover durations between two paper grades. The results from a real industrial case study show that the data-driven model leads to good results in terms of both solution quality and CPU time in comparison to the full-space model.
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