文章基本信息

标题：Research on Optimization of Charge Batch Planning Based on Augmented Lagrangian Relaxation Algorithm
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作者：Congxin Li ; Baolong Yuan ; Junhua Ren 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：1
页码：814-819
DOI：10.1016/j.ifacol.2019.06.162
语种：English
出版社：Elsevier
摘要：AbstractThe steelmaking-continuous casting batch planning plays an important role in the relationship between the Enterprise Resource Planning (ERP) layer and the Manufacturing Execution System (MES) layer. The charge batch planning is one of the important links in the steelmaking-continuous casting batch planning. Efficient optimization of charge batch planning can reduce energy consumption and improve the profit of the enterprise. As the production scale of iron and steel enterprises increases, the number of orders increases exponentially, and the optimization goal becomes more and more complicated, which makes it difficult to ensure the optimization efficiency while ensuring the quality of optimization results. Aiming at this problem, this paper proposes a multi-performance index mixed integer programming model considering actual production, and solves it based on the order decomposition strategy under the linearized Augmented Lagrangian framework. Finally, experiments show that the proposed algorithm can effectively improve the compilation efficiency and quality of the charge batch planning compared with the traditional Lagrangian Relaxation algorithm.
关键词：Keywordscharge batch planningordersmixed integer programmingAugmented Lagrangian framework