The objective of the hot rolling mill is to transform slabs of steel into thin strips which conform to
specific dimensional and metallurgical customer requirements. High performance and flexibility
in the operation is required due to strict customer demands, variable market conditions, and the
drive for continuous improvement.
Historically human schedulers have performed the scheduling and sequencing tasks, however it is
not a reasonable expectation that they consider all the complex objectives required in optimal
production of a hot mill. Therefore, there are significant opportunities for improvement in this
area through the application of mathematical optimization models and solution algorithms.
This work presents a set of models and a solution algorithm for optimal scheduling and
sequencing of production within a hot rolling steel mill. The models and algorithms presented
within this thesis are specifically developed for ArcelorMittal Dofasco’s Hot Strip Mill in
Hamilton, Ontario, Canada. First, a graph theoretic representation of the production block is
developed along with an asymmetric travelling salesman formulation of the sequencing problem.
A slab transition cost function comprised of the hot rolling process objectives is formalized. The
objective of the optimization is to generate a complete block sequence which minimizes the cost
of transitions between slabs thus minimizing the overall cost of production. The Concorde exact
solver is leveraged for the sequencing problem. Second, the scheduling of slabs from inventory
into blocks is considered in addition to sequencing. A methodology for slab clustering is defined.
The novel concept of width-groups is developed and a heuristic algorithm is devised to calculate
an objective for the MILP slab scheduling model. The objective of the scheduling optimization is
to construct a set of blocks which minimize deviation from the calculated width-group design. A
revised sequencing model, updated to reflect the relaxations enabled by the width-group design, is
formulated. Industrial production and offline trials show that the proposed scheduling-sequencing
framework outperforms the human scheduler in all critical performance metrics for both
scheduling and sequencing. A conservative estimate of the reoccurring monetary benefits
available from use of the proposed scheduling-sequencing optimization framework is greater than
$1.2M CAD per year. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/21473 |
| Date | January 2017 |
| Creators | Meyer, Kevin Christopher |
| Contributors | Mahalec, Vladimir, Chemical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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