Hierarchical production management applied to an iron and steel industry

Abstract

A scheduling problem arising in the iron and steel industry is discussed. It concerns the medium planning and the short-term scheduling of three tools: continuous-casting, strip mill and finishing, these three belonging to the hot unit of an iron and steel company. The strip mill is more constrained than the other two, it is therefore called the pivot, the continuous-casting tool is called the upstream tool and the finishing tool is called the downstream tool. A hierarchical approach consisting of two levels is designed. At each level of this hierarchy, the pivot is scheduled first followed by the other tools under the constraints of the pivot schedule. Methods are proposed to solve scheduling problems arising from this approach. Each of the methods is an approximation scheme because of the NP-hardness of the scheduling problems. Some of the approximation schemes are local search methods (simulated annealing improved by some specific techniques), others are specific constructive methods built to solve given problems. An interesting criterion is the following: the schedule of the pivot being given, the schedule of the upstream tool must begin as late as possible and the schedule of the downstream must end as soon as possible in order to minimize the inventory cost between the tools while minimizing the other costs of manufacturing such as the number of profile changes, the number of block changes, etc. The pivot medium-term solving is the most developed part of this paper and it is tested on some generated examples; the generator description and the numerical results are presented.

A case study on the integration of APS and ERP in a steel processing plant

“…For example, scienti®c work on production planning and control in the steel industry, which is also the empirical source for this paper, focuses on designing dedicated algorithms for a speci®c situation and a speci®c part of the steel making process (e.g. Lopez et al 1998, Numao 1994, Miller and Ramnath 1995, Portmann and Rohr 1995, Tamura et al 1998, Daniels 1999, Tang et al 2000, Tang et al 2001. However, in practice, companies implement commercially available standard software packages.…”

Scheduling of die casting operations including high-mix low-volume and line-type production

“…In rough terms, they vary with regard to the applied optimisation methods, i.e., stochastic, deterministic and heuristic methods. The stochastic optimisation approach includes simulated annealing in Portmann and Rohr (1995) and Lin and Ying (2009), evolutionary algorithm in Tanev et al (2004) and Kumar et al (2006a), and tabu search in Ruiz et al (2008). As a deterministic optimisation approach, linear programming models, including Integer Programming (IP) and Mixed Integer Programming (MIP), have been recently used in Kumar et al (2006b), Missbauer et al (2009), Li et al (2011), and Wy and Kim (2010.…”

A finite-capacity production scheduling procedure for a Belgian steel company

“…We cite Tang et al (2002) who give a comparative analysis of the different steel production processes and review their planning and scheduling systems. Most research is dominated by static scheduling methods, as given by, amongst others, the multiple arc network model of Sasidhar and Achary (1991), the greedy constructive method of Petersen et al (1992), the simulated annealing approach of Portmann and Rohr (1995), the implicit enumeration based algorithm of Assaf et al (1997), the linear programming model of Chen and Wang (1997), the goal programming approach of Jacobs et al (1998), the tabu search approach of Lopez et al (1998), the mathematical programming model of Tang et al (2000a), the genetic algorithm of Tang et al (2000b), the combined mathematical programming/heuristic search approach of Cowling and Rezig (2000), the decomposition mathematical programming strategy of Harjunkoski and Grossmann (2001), the Lagrangian relaxation approach of Tang et al (2002), the genetic algorithm of Chen and Wu (2002), the integrated finishing line scheduling approach of Okano et al (2004), the linear and non-linear programming method combination of Neureuther et al (2004) and the combinatorial auction-based approach of Kumar et al (2007). Recently, the focus has shifted towards robust dynamic scheduling which has been investigated by the tabu search based flexible decision support system of Crowling (2003), the multi-agents approaches of Ouelhadj et al (2004) and Crowling et al (2003Crowling et al ( , 2004 and the expert process knowledge analysis technique of Roy et al (2004).…”