Data-Driven Approach to Grade Change Scheduling Optimization in a Paper Machine
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Industrial and Engineering Chemistry Research, Volume 59, issue 17
AbstractThis 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.
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