Record link: 
http://hdl.handle.net/20.500.12708/190764
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Title: 
Job Scheduling for a Multi-Line Steel Hot Rolling Mill With Selectable Furnaces
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Citation: 
Kowalski, M., Kugi, A., & Steinböck, A. (2023). Job Scheduling for a Multi-Line Steel Hot Rolling Mill With Selectable Furnaces. In H. Ishii, Y. Ebihara, J. Imura, & M. Yamakita (Eds.), 22nd IFAC World Congress (pp. 5376–5381). Elsevier. https://doi.org/10.1016/j.ifacol.2023.10.184
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Publisher DOI: 
10.1016/j.ifacol.2023.10.184
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Kowalski, M. 
Kugi, Andreas 
Steinböck, Andreas 
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Organisational Unit: 
E376-02 - Forschungsbereich Komplexe Dynamische Systeme 
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Published in: 
22nd IFAC World Congress 
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Volume: 
56, 2
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Date (published): 
2023
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Event name: 
22nd IFAC World Congress 
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Event date: 
9-Jul-2023 - 14-Jul-2023
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Event place: 
Yokohama, Japan
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Number of Pages: 
6
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Publisher: 
Elsevier
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Keywords: 
Job scheduling; production planning; Heuristic Optimization; modeling of production processes; Simulated Annealing; local search; traveling salesman problem; orienteering problem
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Abstract: 
In a steel hot rolling mill, jobs are scheduled in groups for the production at two different production lines. The process of scheduling involves the selection and the sequencing of jobs as well as the assignment of selectable furnaces to some jobs. An optimal schedule minimizes unproductive times during the production while utilizing necessary retoolings of one production line as best as possible for the production at the other line. The resulting optimization problem is similar to a combination of several traveling salesman problems and orienteering problems. A method is presented to accurately model the alternating production process on both lines, including sequence-dependent setup times, production line retoolings, and the use of an induction furnace with three chambers. Based on this model, unproductive times occurring in a given schedule can be calculated. These times are minimized by a tailored optimization algorithm, which consists of a simulated annealing metaheuristic followed by a local search. The effective application and benefit of this algorithm are demonstrated in a case study.
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Research Areas: 
Mathematical and Algorithmic Foundations: 60%
Computer Science Foundations: 10%
Modeling and Simulation: 30%
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Science Branch: 
2020 - Elektrotechnik, Elektronik, Informationstechnik: 100%
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Appears in Collections:
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