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

Author:

Iannino Vincenzo1,Colla Valentina1,Maddaloni Alessandro2,Brandenburger Jens3,Rajabi Ahmad3,Wolff Andreas3,Ordieres Joaquin4,Gutierrez Miguel4,Sirovnik Erwin5,Mueller Dirk5,Schirm Christoph5

Affiliation:

1. Department of Excellence in Robotics and AI, TeCIP Institute, Scuola Superiore Sant’Anna, Via Moruzzi 1, Pisa, Italy

2. Samovar, Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France

3. VDEh-Betriebsforschungsinstitut, Düsseldorf, Germany

4. Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid, Spain

5. Thyssenkrupp Rasselstein GmbH, Andernach, Germany

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.

Publisher

IOS Press

Subject

Artificial Intelligence,Computational Theory and Mathematics,Computer Science Applications,Theoretical Computer Science,Software

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