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基于分枝搜索的带缓冲集束型设备调度与性能分析

高忠顺 周炳海

高忠顺, 周炳海. 基于分枝搜索的带缓冲集束型设备调度与性能分析. 自动化学报, 2016, 42(1): 81-88. doi: 10.16383/j.aas.2016.c140142
引用本文: 高忠顺, 周炳海. 基于分枝搜索的带缓冲集束型设备调度与性能分析. 自动化学报, 2016, 42(1): 81-88. doi: 10.16383/j.aas.2016.c140142
GAO Zhong-Shun, ZHOU Bing-Hai. The Scheduling and Performance Analysis of Cluster Tools with Buffers Based on Branch Searching. ACTA AUTOMATICA SINICA, 2016, 42(1): 81-88. doi: 10.16383/j.aas.2016.c140142
Citation: GAO Zhong-Shun, ZHOU Bing-Hai. The Scheduling and Performance Analysis of Cluster Tools with Buffers Based on Branch Searching. ACTA AUTOMATICA SINICA, 2016, 42(1): 81-88. doi: 10.16383/j.aas.2016.c140142

基于分枝搜索的带缓冲集束型设备调度与性能分析

doi: 10.16383/j.aas.2016.c140142
基金项目: 

国家自然科学基金 61273035, 71471135

详细信息
    作者简介:

    高忠顺 同济大学机械与能源工程学院工业工程研究所博士研究生.2011年获得同济大学机械与能源工程学院工业工程学士学位.主要研究方向为集成制造系统调度与控制,离散系统的建模、调度仿真和控制技术.E-mail:1110202@tongji.edu.cn

    通讯作者:

    周炳海 同济大学机械与能源工程学院教授.主要研究方向为制造系统/物流系统的建模、调度、仿真和控制技术,生产系统预防性维护建模.本文通信作者.E-mail:bhzhou@tongji.edu.cn

The Scheduling and Performance Analysis of Cluster Tools with Buffers Based on Branch Searching

Funds: 

Supported by National Natural Science Foundation of China 61273035, 71471135

More Information
    Author Bio:

    Ph.D. candidate in the Department of Industrial Engineering, School of Mechanical Engineering, Tongji University. He received his bachelor degree from Tongji University in 2011. His research interest covers scheduling and control of integrated manufacturing systems, discrete system modeling, scheduling, simulation and control

    Corresponding author: ZHOU Bing-Hai Professor at the School of Mechanical Engineering, Tongji University. His research interest covers modeling, simulation, scheduling, and control in integration of manufacturing systems, reliability and preventive maintenance model for systems. Corresponding author of this paper
  • 摘要: 针对带缓冲与驻留约束的集束型设备调度问题,本文提出了一种基于分枝的搜索调度方法.首先,构造问题域的数学模型,在此基础上提出了一种全局搜索算法;然后,通过数学分析证明引出必要的引理,提出一个基于分枝搜索的算法;最后,进行了仿真实验和分析,结果表明本文提出的算法是有效且可行的,同时还表明了带缓冲模块的集束型设备在一定程度上能提高集束型设备的产能.
  • 图  1  带缓冲的集束型设备示意图

    Fig.  1  The cluster tool with buffers

    图  2  搜索决策树

    Fig.  2  The decision tree for searching

    图  3  两种算法所需的时间

    Fig.  3  Running time for the two algorithms

    图  4  两种算法的GAP值对比

    Fig.  4  GAP for the two algorithms

    图  5  算法结果的比较

    Fig.  5  The comparison between the algorithms

    图  6  生产率提高与搬运模块繁忙程度的关系

    Fig.  6  The relationship of improvement rates with the busy degree of TM

    图  7  生产率提高与加工时间之间的关系

    Fig.  7  The relationship of improvement rates with processing time of PMs

    表  1  $s=s_5$ 时相应参数的值

    Table  1  The relevant parameter when $s=s_5$

    $k$ 0 1 2 3
    $\varphi(k)$ 0 2 1 3
    $\varphi^-(k)$ 0 2 1 3
    $u_k5$ $\setminus$ 0 1 0
    ${{\lambda }_{k}}$ 1 1 1 1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2014-03-11
  • 录用日期:  2015-10-28
  • 刊出日期:  2016-01-01

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