| 引用本文: | 轩华,李冰,王薛苑,徐春秋.带运输考虑的多阶段动态可重入混合流水车间调度[J].控制理论与应用,2018,35(3):357~366.[点击复制] |
| XUAN Hua,LI Bing,WANG Xue-yuan,XU Chun-qiu.Multi-stage dynamic reentrant hybrid flowshop scheduling with transportation consideration[J].Control Theory and Technology,2018,35(3):357~366.[点击复制] |
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| 带运输考虑的多阶段动态可重入混合流水车间调度 |
| Multi-stage dynamic reentrant hybrid flowshop scheduling with transportation consideration |
| 摘要点击 2257 全文点击 1218 投稿时间：2017-07-08 修订日期：2018-01-31 |
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| DOI编号 10.7641/CTA.2017.70465 |
| 2018,35(3):357-366 |
| 中文关键词 动态可重入混合流水车间 运输时间 拉格朗日松弛 改进动态规划 异步次梯度优化 |
| 英文关键词 dynamic reentrant hybrid flowshop transportation time Lagrangian relaxation improved dynamic programming interleaved subgradient optimization |
| 基金项目 教育部人文社会科学研究项目(15YJC630148), 国家自然科学基金项目(U1604150), 郑州大学优秀青年教师发展基金项目(1421326092), 河南省高 等学校重点科研项目(17A520058)资助. |
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| 中文摘要 |
| 可重入混合流水车间调度允许一个工件多次进入某些加工阶段, 它广泛出现在许多工业制造过程中, 如半
导体制造、印刷电路板制造等. 本文研究了带运输时间的多阶段动态可重入混合流水车间问题, 目标是最小化总加
权完成时间. 针对该问题, 建立了整数规划模型, 进而基于工件解耦方式提出了两种改进的拉格朗日松弛(LR)算法.
在这些算法中, 设计了动态规划的改进策略以加速工件级子问题的求解, 提出了异步次梯度法以得到有效的乘子更
新方向. 测试结果说明了所提出的两种改进算法在解的质量和运行时间方面均优于常规LR算法, 两种算法都能在
可接受的计算时间内得到较好的近优解. |
| 英文摘要 |
| Reentrant hybrid flowshop scheduling is widely found in many industries such as semiconductor manufacturing
and printed circuit board fabrication, where a job visits some processing stages for several times. A multi-stage dynamic
reentrant hybrid flowshop problem with transportation time is studied with the objective of minimizing total weighted completion
time. Then an integer programming model is formulated and two improved Lagrangian relaxation (LR) algorithms
are presented based on job decoupling. In these algorithms, dynamic programming is improved to speedup the resolution
of job-level subproblems and interleaved subgradient optimization is designed to obtain an effective multiplier updating
direction. Testing results demonstrate that the two proposed LR algorithms outperform the traditional LR in terms of solution
quality and running time. Both of the two algorithms could get better near-optimal schedules within an acceptable
computational time. |
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