Production Scheduling of a Large-Scale Steelmaking Continuous Casting Process via Unit-Specific Event-Based Continuous-Time Models: Short-Term and Medium-Term Scheduling
The scheduling of steelmaking continuous-casting (SCC) processes is of major importance in iron and steel operations, since it is often a bottleneck in iron and steel production. Optimal scheduling of SCC processes can increase profit, minimize production cost, reduce material and energy consumption...
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Veröffentlicht in: | Industrial & engineering chemistry research 2012-05, Vol.51 (21), p.7300-7319 |
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Sprache: | eng |
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Zusammenfassung: | The scheduling of steelmaking continuous-casting (SCC) processes is of major importance in iron and steel operations, since it is often a bottleneck in iron and steel production. Optimal scheduling of SCC processes can increase profit, minimize production cost, reduce material and energy consumption, and improve customer satisfaction. Scheduling of SCC processes is challenging, because of its combinatorial nature, complex practical constraints, and strict requirements on material continuity and flow time, as well as the technological requirements to ensure practical feasibility of the resulting scheduling. In this paper, we first develop a novel unit-specific event-based continuous-time mixed-integer linear optimization (MILP) model for this problem and incorporate several realistic operational features. Then, we extend the rolling horizon approach proposed by Lin et al. [Lin et al. Ind. Eng. Chem. Res. 2002, 41, 3884–3906] and Janak et al. [Janak et al. Ind. Eng. Chem. Res. 2006, 45, 8234–8252] to solve this large-scale and complex optimization problem. Four large-scale industrial problems are used to illustrate the efficiency and effectiveness of the proposed formulation and rolling horizon approach. The computational results show that the extended rolling horizon approach successfully solves the large-scale case studies and results in the same or better integer solution than that obtained from directly solving the entire scheduling model. |
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ISSN: | 0888-5885 1520-5045 |
DOI: | 10.1021/ie2015944 |