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Many-objective harmony search for integrated order planning in steelmaking-continuous casting-hot rolling production of multi-plants

机译:多厂炼钢-连铸-热轧生产中多目标和谐搜索的集成订单计划

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This paper investigates a challenging problem of integrated order planning (IOP) in steelmaking-continuous casting-hot rolling production of multiple plants with consideration of four conflicting objectives. The objective functions refer to the earliness/tardiness ratio, the non-hot charge ratio and the imbalance ratio of production capacity utilisation corresponding to SCC plants and HR Plants. The IOP guided by the integration strategy, which includes the vertical integration of production stages and the horizontal integration of steel plants, is regarded as a large-scale many-objective optimisation problem. To deal with the difficulty of large-scale decision variables, we introduce a new concept named 'order-set' for modelling. In addition, a novel knee point-driven many-objective global-best harmony search (KGHS) algorithm, mainly integrating a KGHS process and a new knee point-driven Pareto optimisation, is developed to tackle this many-objective problem. The proposed model and algorithm were tested with benchmarks and real production data. Experiments demonstrate that the proposed approach generates effective solutions superior to those generated by the other popular many-objective optimisation methods.
机译:本文考虑了四个相互矛盾的目标,研究了炼钢-连铸-热轧多厂生产中的集成订单计划(IOP)的挑战性问题。目标函数是指与SCC工厂和HR工厂相对应的早期/延迟比率,非热装比率和产能利用不平衡比率。集成策略指导下的IOP,包括生产阶段的垂直集成和钢厂的水平集成,被认为是一个大规模的多目标优化问题。为了解决大规模决策变量的困难,我们引入了一个名为“订单集”的新概念进行建模。此外,针对此多目标问题,开发了一种新颖的膝点驱动多目标全局最佳和谐搜索(KGHS)算法,该算法主要集成了KGHS过程和新的膝点驱动的帕累托优化。所提出的模型和算法已通过基准测试和实际生产数据进行了测试。实验表明,该方法产生的有效解决方案优于其他流行的多目标优化方法。

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