1. Data-Driven Approach to Grade Change Scheduling Optimization in a Paper Machine
- Author
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Tewodros Deneke, Teemu J. Ikonen, Hossein Mostafaei, Jason Kramb, Iiro Harjunkoski, Keijo Heljanko, Department of Chemical and Metallurgical Engineering, Process Control and Automation, UPM Communication Papers, University of Helsinki, Aalto-yliopisto, Aalto University, Department of Computer Science, and Helsinki Institute for Information Technology
- Subjects
Decision support system ,business.product_category ,Computer science ,CONTINUOUS-TIME ,General Chemical Engineering ,MODELS ,Scheduling (production processes) ,215 Chemical engineering ,02 engineering and technology ,General Chemistry ,021001 nanoscience & nanotechnology ,FRAMEWORK ,Industrial engineering ,Industrial and Manufacturing Engineering ,Scheduling (computing) ,Data-driven ,Paper machine ,020401 chemical engineering ,SCOPE ,0204 chemical engineering ,0210 nano-technology ,business ,Computer Science::Operating Systems - 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.
- Published
- 2020