北京航空航天大学学报 ›› 2016, Vol. 42 ›› Issue (3): 435-443.doi: 10.13700/j.bh.1001-5965.2015.0170

• 论文 • 上一篇    下一篇

考虑柔性检修计划的圆钢热轧批量调度

王雷1,2, 赵秋红2, 许绍云3   

  1. 1. 中国刑事警察学院治安学系, 沈阳 110854;
    2. 北京航空航天大学经济管理学院, 北京 10008;
    3. 中国科学院微电子研究所, 北京 100029
  • 收稿日期:2015-03-24 出版日期:2016-03-20 发布日期:2015-09-17
  • 通讯作者: 赵秋红,Tel.:010-82316181 E-mail:qhzhao@buaa.edu.cn E-mail:qhzhao@buaa.edu.cn
  • 作者简介:王雷 男,博士,讲师。主要研究方向:智能优化算法、应急管理。Tel.:024-86982210 E-mail:leonwang521@126.com;赵秋红 女,博士,教授,博士生导师。主要研究方向:启发式算法、应急管理。Tel.:010-82316181 E-mail:qhzhao@buaa.edu.cn
  • 基金资助:
    国家自然科学基金(71271013,71471006);辽宁省社会科学规划基金(L15AGL016)

Hot-rolling batch scheduling in round steel production with flexible maintenance planning

WANG Lei1,2, ZHAO Qiuhong2, XU Shaoyun3   

  1. 1. Department of Public Order, National Police University of China, Shenyang 110854, China;
    2. School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 10008;
    3. Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
  • Received:2015-03-24 Online:2016-03-20 Published:2015-09-17
  • Supported by:
    National Natural Science Foundation of China (71271013, 71471006);Social Science Planning Foundation of Liaoning Province in China (L15AGL016)

摘要: 针对考虑柔性检修计划的圆钢热轧批量调度问题,构建了以最小化最大完工时间、订单提前及拖期总时长为目标函数的整数规划模型,用以制定有效的机器检修与批量生产协作计划。结合模型特征,提出一种改进多目标粒子群算法(IMPSO)实现求解。算法采用基于混沌加权适应度计算的插入式方法生成初始粒子群体;根据问题约束特征,设计修复规则对群体进化过程中产生的不可行粒子进行修复;采用精英策略保留算法迭代过程中的优势个体,并根据精英集合为每个粒子选择更新所需的极值;针对问题变量的离散特征,引入基于遗传操作的粒子更新方式。实验结果表明,模型和算法是可行和有效的。

关键词: 热轧批量调度, 柔性检修计划, 多目标优化, 粒子群算法(PSO), 圆钢

Abstract: A hot-rolling batch scheduling problem of round steel with flexible maintenance planning was studied. For obtaining an effective cooperative scheduling with machine maintenance and batch production, a multi-objective integer programming model was built with the objectives to minimize the makespan, the earliness and tardiness of orders. With the consideration on the feature of the model, an improved multi-objective particle swarm optimization (IMPSO) algorithm was proposed to solve the problem. In the proposed algorithm, an insertion algorithm based on fitness assignment with chaos weighting was designed to generate the initial solution. According to the constraints in the model, some rules were proposed to repair unreasonable solutions emerging in the genetic progress of the population. With the elitist strategy, advanced individuals are preserved in evolution process, and the extremums for every individual's updating were also selected from elite set. In addition, with considering the discrete characteristic of variables, genetic operators were introduced to update particles. Experimental results show that the model and algorithm are feasible and effective.

Key words: hot-rolling batch scheduling, flexible maintenance planning, multi-objective optimization, particle swarm optimization (PSO), round steel

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