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Abstract
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 language | English |
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Pages (from-to) | 8281-8294 |
Number of pages | 14 |
Journal | Industrial and Engineering Chemistry Research |
Volume | 59 |
Issue number | 17 |
Early online date | 13 Mar 2020 |
DOIs | |
Publication status | Published - 29 Apr 2020 |
MoE publication type | A1 Journal article-refereed |
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Dive into the research topics of 'Data-Driven Approach to Grade Change Scheduling Optimization in a Paper Machine'. Together they form a unique fingerprint.Projects
- 1 Finished
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Synergistic and intelligent process optimization
Harjunkoski, I. (Principal investigator), Mohammadi, M. (Project Member), Ikonen, T. (Project Member) & Mostafaei, H. (Project Member)
01/01/2018 → 31/12/2019
Project: Academy of Finland: Other research funding