引用本文:张浩,朱云龙,亓祥波.多目标根系生长算法在高精铜锭熔炼作业调度中的应用[J].控制理论与应用,2018,35(1):121~128.[点击复制]
ZHANG Hao,ZHU Yun-long,QI Xiang-bo.Application of multi-objective root growth algorithm in job scheduling of the smelting process for high-precision copper ingot[J].Control Theory and Technology,2018,35(1):121~128.[点击复制]
多目标根系生长算法在高精铜锭熔炼作业调度中的应用
Application of multi-objective root growth algorithm in job scheduling of the smelting process for high-precision copper ingot
摘要点击 2516  全文点击 1180  投稿时间：2016-08-15  修订日期：2017-08-14
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DOI编号  10.7641/CTA.2017.60615
  2018,35(1):121-128
中文关键词  作业调度  多目标优化  根系生长  高精铜铸锭  熔炼过程
英文关键词  Job scheduling  multi-objective optimization  root growth  high-precision copper ingot  smelting process
基金项目  省自然科学基金
作者单位E-mail
张浩* 中国科学院沈阳自动化研究所 zhanghao@sia.cn 
朱云龙 中国科学院沈阳自动化研究所  
亓祥波 中国科学院沈阳自动化研究所  
中文摘要
      本文提出一种基于植物根系生长行为的自适应多目标算法(MORGA), 用于求解高精度铜铸锭熔炼过程中的作业调度优化问题。首先，根据铜铸锭熔炼生产线现有的生产能力和熔炼工艺，以达到对客户承诺的交货期、降低生产成本的目的，建立以最小化生产总时间和订单未编入计划而受到的总惩罚值为目标的作业调度优化模型。然后，以植物根系分化式生长行为的数学仿真模型为基础，融入多目标优化策略，提出自适应多目标优化算法，设计编码规则，使其能够有效求解高精度铜铸锭熔炼作业调度模型。最后，利用实际生产数据对MORGA进行验证，并与经典多目标优化算法NSGAⅡ和MOPSO比较，MORGA获得了更优的结果。
英文摘要
      This paper proposes a novel multi-objective root growth algorithm (MORGA) based on self-adaptive behavior of plant root growth. It can solve job scheduling optimization problem in the smelting process of high-precision copper ingot. First, a job scheduling model for optimization is established on the existing production capacity and melting technology of smelting production line for copper ingot. The model is formulated with two objectives of minimizing production time and penalty value for the plans not containing some orders, which can meet clients' delivery date and reduce production cost. Then, MORGA is formulated based on mathematical simulation model for plant root growth behavior with multi-objective strategy. A new encoding rule for the algorithm is designed to solve the job scheduling model effectively. The experiment results using the actual data in production show that MORGA is robust and effective. MORGA can obtain better solutions compared to NSGAⅡ and MOPSO when solving the model.