| 求解可重入作业车间调度问题的改进离散微粒群优化算法 |
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| 引用本文： | 求解可重入作业车间调度问题的改进离散微粒群优化算法.求解可重入作业车间调度问题的改进离散微粒群优化算法[J].山东科学,2017,30(6):105-119. |
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| 作者姓名： | 求解可重入作业车间调度问题的改进离散微粒群优化算法 |
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| 作者单位： | 山东省科学院海洋仪器仪表研究所，山东省海洋仪器仪表科技中心，山东 青岛 26600 |
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| 基金项目： | 青岛市市南区科技发展基金(2016-2-012-ZH ) |
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| 摘 要： | 本文针对可重入作业车间调度问题,对离散微粒群算法的搜索方式进行改进,混合一种变异机制,并结合Interchange邻域局部搜索机制,设计与开发有效的混合离散微粒群算法。通过实验仿真结果的比较,有力地证明了所提算法的有效性。
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| 关 键 词： | 作业车间 离散微粒群算法 局部搜索 |
| 收稿时间： | 2017-01-03 |
Improved discrete particle swarm optimization algorithm to solve the reentrant job shop scheduling problem |
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| WAN Jing.Improved discrete particle swarm optimization algorithm to solve the reentrant job shop scheduling problem[J].Shandong Science,2017,30(6):105-119. |
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| Authors: | WAN Jing |
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| Institution: | Shandong Technological Center of Oceanographic Instrumentation，Institute of Oceanographic Instrumentation, Shandong Academy of Sciences，Qingdao 26600, China |
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| Abstract: | In this paper, by combining the existing discrete particle swarm optimization algorithm with a small but effective local search, an effective hybrid discrete particle swarm optimization algorithm was formed. This paper also discussed how to fuse the local search to optimize the discrete particle swarm. And the effectiveness of the proposed algorithm has been proved by the comparison of the simulation results. |
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| Keywords: | local search discrete particle swarm optimization algorithm job-shop |
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