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

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.

DOI

10.1021/acs.iecr.9b06907

Citation

Mostafaei , H , Ikonen , T , Kramb , J , Deneke , T , Heljanko , K & Harjunkoski , I 2020 , ' Data-Driven Approach to Grade Change Scheduling Optimization in a Paper Machine ' , Industrial and Engineering Chemistry Research , vol. 59 , no. 17 , pp. 8281-8294 . https://doi.org/10.1021/acs.iecr.9b06907

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202006254079

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[article-cris] Kemian tekniikan korkeakoulu / CHEM

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