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Scheduling a Galvanizing Line by Ant Colony Optimization

  • Silvino Fernandez
  • Segundo Alvarez
  • Diego Díaz
  • Miguel Iglesias
  • Borja Ena
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8667)

Abstract

In this paper, we describe the successful use of ACO to schedule a real galvanizing line in a steel making company, and the challenge of putting the algorithm to use in an industrial environment. The sequencing involves several calculations in parallel to figure out the best sequence considering the evolution of each important parameter: width, thickness, thermal cycle, weldability, etc.

For solving this combinatorial (NP-hard) problem, new necessity arose to develop an intelligent algorithm able to optimize the scheduling, avoiding traditional manual calculations. Hence, ACO is proposed to translate the scheduling rules and current criteria into a set of technical constraints and cost functions to assure a good solution in a short calculation time.

Keywords

Cost Function Particle Swarm Optimization Swarm Intelligence Galvanize Steel Schedule Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Silvino Fernandez
    • 1
  • Segundo Alvarez
    • 1
  • Diego Díaz
    • 1
  • Miguel Iglesias
    • 1
  • Borja Ena
    • 1
  1. 1.ArcelorMittal Global R&D AsturiasAvilésSpain

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