An Application of Systems Modelling in Steel Production Scheduling

Wright, Jeff R.; Houck, Mark H.

doi:10.1007/978-3-662-21877-8_75

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Wright [12] formulated a multi-objective mathematical model for scheduling the hot rolling sequences to minimize penalties as well as deviations from target delivery dates over the course of a campaign. Balas and Martin [3] [4] reduced the hot rolling production scheduling problems to the knapsack constraint problems and prize collecting TSP models.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Heuristics Scheduling Algorithm for Hot Strip Mill

Wang

Gao

Zeng

2007

Third International Conference on Natural Computation (ICNC 2007) Vol V

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This paper introduces the scheduling algorithm of a computer simulation and scheduling system for hot strip mill. The mathematical model of Hot Strip Mill Scheduling Problem (HSMSP) is formulated. A hybrid heuristics method is proposed, based on parallel strategy, which integrates domain expertise, Genetic Algorithm(GA) and Tabu Search(TS). Domain expertise is used to classify and sort production orders to generate initial solutions in parallel. GA is used to improve the initial solutions. And TS attempt to accelerate the convergence of the solutions by adopting swaps of 'single slab' and 'slab section'. 'Single slab' and 'slab section', which we firstly introduced, are new adjacent solutions (neighborhood). Simulation results have shown that the proposed method is feasible and efficient.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid Heuristics Scheduling Algorithm for Hot Strip Mill

Wang

Gao

Zeng

2007

Third International Conference on Natural Computation (ICNC 2007) Vol V

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“…In particular the complexity of exact algorithms makes a global approach impractical. Many approaches have been developed for iron and steel factories: some of them used operations research tools, other used artificial intelligence techniques (see for example: Nakagawa et al, 1980;Vasko and Friedel, 1982;Yasuda et al, 1984;Wright and Houck, 1985;Jacobs et al, 1988;Nambiar and Acharya, 1988;Guay, 1990;Lu et al, 1990;Manheim et al, 1990;Shah et al, 1990;Tabucanon and Sasiwong, 1991;Thorsen and Vidal, 1991;Portmann and Camps, 1993). Unfortunately developments made for one company are not usable by another.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical production management applied to an iron and steel industry

Portmann

Rohr

1995

J Intell Manuf

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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.

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