Data-Driven Approach to Grade Change Scheduling Optimization in a Paper Machine

Hossein Mostafaei, Teemu Ikonen, Jason Kramb, Tewodros Deneke, Keijo Heljanko, Iiro Harjunkoski*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
20 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)8281-8294
Number of pages14
JournalIndustrial and Engineering Chemistry Research
Issue number17
Early online date13 Mar 2020
Publication statusPublished - 29 Apr 2020
MoE publication typeA1 Journal article-refereed


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