1、

This paper designs application of the algorithm in optimizing multi-objective flexible job-shop scheduling. ( 3) A simulation experiment is carried out to illustrate that the proposed hybrid genetic algorithm could solve the general multi-objective flexible job shop scheduling problem problem effectively.

对该算法在多目标柔性作业车间调度优化的应用进行了详细设计。(3)仿真结果表明,提出的混合遗传算法可以有效解决一般模式下的多目标柔性作业车间调度问题。

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2、

The job scheduling of Batch Processing System is analyzed by using Process Model in this paper.

批处理系统建模是其性能评价、仿真、调度以及控制研究的基础.

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3、

In conformity to the general job shop scheduling problem, this paper constructs a gap network consisting of nodes, shortest paths and adjacent paths.

对应于一般单件车间排序问题,构造了一种由节点、最短路径和相邻路径组成的隙网络。

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4、

Finally, a job shop scheduling software is developed using Visual C++ 6.0.Of the above series of researches on production scheduling, static and dynamic scheduling problems on multi-resources constraints are firstly solved in the world.

最后,用Visual c++6.0开发了实用性的车间生产调度软件。在上述生产调度问题的系列化研究中,多资源调度静态和动态生产调度问题的研究属世界首例。

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5、

Discussed how to use JDL to implement job definition and job scheduling functions in current JMS and how to use JDL in a future JMS and the advantages brought by using of JDL.

论述了如何用JDL语言实现现有作业管理系统中的作业定义和作业调度功能,并对该语言在未来的作业管理系统中的应用以及由此带来的优点进行了论述。

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6、

The results have shown the effectiveness of particle swarm optimisation for flexible job shop scheduling.

实验结果表明粒子群算法在柔性工作车间调度问题的应用上是十分有效的.

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8、

Secondly, considering communication overtime, a extended compact mode scheduling algorithm based on job rate monotonic ( JRM) is presented to schedule all real time tasks in order to meet their time constraints and precedence constraints.

其次,考虑单网段通信任务共享一个传输介质而引起的通信超时,提出了基于作业速率单调优先级算法的扩展紧凑模式的任务调度算法,以满足实时任务的时间约束和次序约束。

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9、

Way of salving job-shop scheduling problem by means of Hopfield neural network

用Hopfield神经网络解决作业车间调度问题

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10、

User demands; GTS is a user-level job scheduling system on Linux.

GTS是一个用户级的任务调度系统,它建立于Linux之上,主要负责对用户任务的调度。

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11、

Hadoop job scheduling in a cloud environment brings new challenges to academia and industry.

云环境下Hadoop作业调度的研究给学术界和工业界带来了新的挑战。

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12、

Genetic Algorithm and Adaptive Neural Network Hybrid Method for Job-shop Scheduling Problems

用遗传算法与自适应神经网络混合方法解Job-shop调度问题

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