A hybrid approach for improving the flexibility of production scheduling in flat steel industry

  • Vincenzo Iannino
  • , Valentina Colla
  • , Alessandro Maddaloni
  • , Jens Brandenburger
  • , Ahmad Rajabi
  • , Andreas Wolff
  • , Joaquin Ordieres
  • , Miguel Gutierrez
  • , Erwin Sirovnik
  • , Dirk Mueller
  • , Christoph Schirm

Research output: Contribution to journalArticlepeer-review

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 delays are minimized. Therefore, an ever-increasing attention is paid toward production optimization through efficient scheduling strategies in the scientific and industrial communities. This paper proposes a hybrid approach to improve the flexibility of production scheduling in steelworks producing flat steel products. Such approach combines three methods holding different scopes and modelling different aspects: an auction-based multi-agent system is applied to face production uncertainties, multi-objective mixed-integer linear programming is used for global optimal scheduling of resources under steady conditions, while a continuous flow model copes with long-term production scheduling. According to the obtained simulation results, the integration and combination of these three approaches allow scheduling production in a flexible way by providing the capability to adapt to different production conditions.

Original languageEnglish
Pages (from-to)367-387
Number of pages21
JournalIntegrated Computer-Aided Engineering
Volume29
Issue number4
DOIs
Publication statusPublished - 1 Jan 2022

Keywords

  • Flat steel production scheduling
  • continuous flow model
  • hybrid approach
  • multi-agent systems
  • multi-objective mixed-integer linear programming

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