2022
DOI: 10.3233/ica-220685
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A hybrid approach for improving the flexibility of production scheduling in flat steel industry

Abstract: Nowadays the steel market is becoming ever more competitive for European steelworks, especially as far as flat steel products are concerned. As such competition determines the price products, profit can be increased only by lowering production and commercial costs. Production yield can be significantly increased through an appropriate scheduling of the semi-manufactured products among the available sub-processes, to ensure that customers’ orders are timely completed, resources are optimally exploited, and dela… Show more

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Cited by 5 publications
(2 citation statements)
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“…The combination of transfer learning (TL) and RL has attracted considerable attention from researchers in order to improve the efficiency of RL, such as policy distillation [63,64], and learning from demonstrations [65,66], and so on. The combination of various methods can compensate for each other's shortcomings [67,68]. However, the knowledge of complex agents can easily fail to be transferred since the inconsistent state distribution among data of different classes, worse still may result in negative impact.…”
Section: Related Workmentioning
confidence: 99%
“…The combination of transfer learning (TL) and RL has attracted considerable attention from researchers in order to improve the efficiency of RL, such as policy distillation [63,64], and learning from demonstrations [65,66], and so on. The combination of various methods can compensate for each other's shortcomings [67,68]. However, the knowledge of complex agents can easily fail to be transferred since the inconsistent state distribution among data of different classes, worse still may result in negative impact.…”
Section: Related Workmentioning
confidence: 99%
“…Scheduling [1,2] is a classical manufacturing problem that consists in organizing the execution of a set of tasks or operations to make the best use of the available resources under some constraints in order to fulfill some objectives. Solving scheduling problems is essential not only in industrial applications [3,4], in particular in Industry 4.0 and 5.0 [5], but also in healthcare [6,7], computing infrastructures [8] or education [9]. Besides its undeniable applicability, scheduling also poses a computational challenge, since many of its problems are NP-Hard [10].…”
Section: Introductionmentioning
confidence: 99%