• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >A genetic algorithm-based scheduling system for dynamic job shop scheduling problems.
【24h】

A genetic algorithm-based scheduling system for dynamic job shop scheduling problems.

机译：基于遗传算法的动态作业车间调度问题调度系统。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In manufacturing systems, inputs of resources, such as materials, labor, machines, energy, and information, are transformed to finished products for output. Managing the transformation process in an efficient and effective manner has been recognized as essential to survival in the current competitive marketplace. Among the operations-management functions, scheduling, which is the last step before operations plans are converted into productive activities, is concerned with allocating available resources to specific jobs and orders in the best manner to meet the operations objectives.; The goal of this research is to develop an efficient genetic algorithm-based scheduling system to address a general scheduling problem--the dynamic job shop scheduling problem. Based on the Giffler and Thompson algorithm, we have extended that approach by providing two new operators, THX crossover and mutation, which better transmit temporal relationships in the schedule. The approach produced excellent results on standard benchmark job shop scheduling problems. We further tested many models and scales of parallel genetic algorithms in the context of job shop scheduling problems. In our experiments, the hybrid model consisting of coarse-grain GAs connected in a fine-grain-GA-style topology performed best, appearing to integrate successfully the advantages of coarse-grain and fine-grain GAs.; In the simulation study, the objective functions examined were weighted flow time, maximum tardiness, weighted tardiness, weighted lateness, weighted number of tardy jobs, and weighted earliness plus weighted tardiness. We further tested the approach under various manufacturing environments with respect to the machine workload, imbalance of machine workload, and due date tightness. The results indicate that the approach performs well and is robust with regard to the objective function and the manufacturing environment in comparison with priority rule approaches.
机译：在制造系统中，资源的输入（例如材料，人工，机器，能源和信息）被转换为成品以进行输出。在当前竞争激烈的市场中，以高效，有效的方式管理转型过程对于生存至关重要。在运营管理职能中，调度是将运营计划转换为生产活动之前的最后一步，它涉及以最佳方式将可用资源分配给特定作业和订单，以实现运营目标。这项研究的目的是开发一种基于遗传算法的高效调度系统，以解决一般的调度问题-动态作业车间调度问题。基于Giffler和Thompson算法，我们通过提供两个新的运算符THX交叉和变异扩展了该方法，它们可以更好地在进度表中传递时间关系。该方法在标准基准作业车间调度问题上产生了出色的结果。我们在车间调度问题中进一步测试了并行遗传算法的许多模型和规模。在我们的实验中，由以粗粒度GA样式拓扑连接的粗粒度GA组成的混合模型效果最好，似乎成功地整合了粗粒度GA和细粒度GA的优势。在模拟研究中，检查的目标函数是加权流动时间，最大拖延时间，加权拖后时间，加权迟到时间，加权迟到工作数量以及加权早期和加权拖后时间。我们针对机器工作量，机器工作量不平衡和到期日紧缺性，在各种制造环境下进一步测试了该方法。结果表明，与优先级规则方法相比，该方法在目标功能和制造环境方面表现良好且鲁棒。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号