考虑元素烧损和仓储成本的特种铝合金配料优化
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中国科学院沈阳自动化研究所

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中图分类号:

TP273

基金项目:

国家自然科学基金项目(61803367)


Burdening optimization of special aluminum alloy with melting loss and storage costs
Author:
Affiliation:

Shenyang Institute of Automation, Chinese Academy of Sciences

Fund Project:

The National Natural Science Foundation of China(61803367)

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    摘要:

    配料计算是特种铝合金熔炼的重要准备工序,直接影响产品最终性能。为提高产品质量和配料效率,降低原料和仓储物流成本,建立考虑元素烧损和旧料循环利用等因素的特种铝合金配料优化模型。针对该模型的目标多样性和非线性等特点,设计以投料量和投料时间为决策变量的实数编码规则,提出一种基于第III代非支配遗传算法并融入分布式估计策略的多目标优化算法用于求解该模型。通过基于真实生产数据的仿真实验进行模型和算法验证。实验结果表明,该模型能够有效地解决特种铝合金配料优化问题;与传统的多目标优化算法相比,所提出的求解算法能够获得更优的结果。

    Abstract:

    Burdening calculation is the important preparatory process for special aluminum smelting, which affects production efficiency and product quality directly. To improve production quality and burdening efficiency and reduce the costs of material and storage logistics, burdening optimization model of special aluminum alloy is built with melting loss of chemical elements and cyclic utilization of waste products. For multi-objective and nonlinear characteristics of this model, real code rule is designed that the amount of raw materials and charging time are decision variables, and the multi-objective algorithm based on NSGA-III are proposed to solve it with distributed estimation strategy. The model and algorithm are verified by means of the simulation experiment with real production data. The experimental result shows this optimization model and the proposed algorithm meets the requirements of enterprises and can solve the burdening optimization problem for special aluminum alloy. The proposed algorithm can obtain better solutions compared to NSGA-III when solving the model.

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历史
  • 收稿日期:2021-08-25
  • 最后修改日期:2022-04-22
  • 录用日期:2022-04-27
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