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Production Engineering
Erschienen in: Production Engineering 1/2019

31.10.2018 | Production Management

A decision support flexible scheduling system for continuous galvanization lines using genetic algorithm

verfasst von: Miri Weiss Cohen, Hila Foxx, Shimon Ben Alul

Erschienen in: Production Engineering | Ausgabe 1/2019

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Abstract

An important, complex problem for logistic optimization in the steel manufacturing plants is obtaining a flexible and adaptive scheduling system for a continuous galvanization line (CGL). The problem tackled in this work involves several constraints and characteristics inspired by real-life manufacturing goals. Given the complexity of the problem, which belongs to the class of NP-hard problems, a genetic algorithm (GA) methodology was developed, combining a penalty procedure defined for constraints with assigned weights for different characteristics of coils. By enlisting the ability and flexibility of GAs, a set of parameters are analyzed to achieve the best results for practical applications. This scheduling solution predicts a CGL sequences with a minimum number of coil transitions, to improve productivity and reduce costs.
Vorheriger Artikel Reinforcement learning for opportunistic maintenance optimization
Nächster Artikel Proposal method for the classification of industrial accident scenarios based on the improved principal components analysis (improved PCA)

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Metadaten
Titel
A decision support flexible scheduling system for continuous galvanization lines using genetic algorithm
verfasst von
Miri Weiss Cohen
Hila Foxx
Shimon Ben Alul
Publikationsdatum
31.10.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Production Engineering / Ausgabe 1/2019
Print ISSN: 0944-6524
Elektronische ISSN: 1863-7353
DOI
https://doi.org/10.1007/s11740-018-0856-6

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