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3038. Integrated optimisation for aluminium rolling
Invited abstract in session MD-49: Lot-sizing with industrial applications II, stream Lot Sizing, Lot Scheduling and Production Planning.
Monday, 14:30-16:00Room: M1 (building: 101)
Authors (first author is the speaker)
| 1. | Georgios Zois
|
| Management Science and Technology, Athens University of Economics and Business | |
| 2. | Ioannis Avgerinos
|
| Department of Management Science and Technology, Athens University of Economics and Business | |
| 3. | Apostolos Besis
|
| R&D, Elval, aluminium rolling division of ElvalHalcor SA | |
| 4. | Yiannis Mourtos
|
| Management Science & Technology, Athens University of Economics & Business | |
| 5. | Athanasios Psarros
|
| Elval, aluminium rolling division of ElvalHalcor SA | |
| 6. | Stavros Vatikiotis
|
| Department of Management Science and Technology, Athens University of Economics and Business |
Abstract
This work focuses on the aluminium production scheduling problem of a leading European Copper and Aluminium industry. The problem forms an interesting variant of flexible flow-shop scheduling consisting of two successive stages, the preheating and the hot rolling of aluminium slabs i.e.,: A set of slabs arrives over time and (in the preheating stage) is assigned to a set of parallel furnaces. Once preheating is completed, each slab is unloaded and immediately transferred to a hot rolling mill.
Each furnace operates in a FIFO manner under slabs' eligibilities and capacity constraints, while the hot rolling mill can handle a single slab every time instant, under predefined quality conditions regarding the minimum and maximum number of slabs per production cycle. Each slab is accompanied with preheating and rolling times and is subject to quality restrictions determined by the sequence of slabs in the same production cycle. Slabs also require specific preheating operations and can only be preheated with slabs of compatible operations in the same furnace. We propose an integrated optimisation framework, comprised by an exact method for each production cycle, which combines a MILP and CP formulation for both stages, and a sequential heuristic approach for planning over multiple cycles. We provide experimentation on real instances, showing the versatility of our approach in terms of supporting different planning approaches thus maximising quality of production and machines usage
Keywords
- Manufacturing
- Programming, Mixed-Integer
- Scheduling
Status: accepted
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