• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on industrial informatics >Energy-Efficient Scheduling of Distributed Flow Shop With Heterogeneous Factories: A Real-World Case From Automobile Industry in China
【24h】

Energy-Efficient Scheduling of Distributed Flow Shop With Heterogeneous Factories: A Real-World Case From Automobile Industry in China

机译：具有异质工厂的分布式流量店的节能调度：中国汽车工业的真实案例

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

Distributed flow shop scheduling of a camshaft machining is an important optimization problem in the automobile industry. The previous studies on distributed flow shop scheduling problem mainly emphasized homogeneous factories (shop types are identical from factory to factory) and economic criterion (e.g., makespan and tardiness). Nevertheless, heterogeneous factories (shop types are varied in different factories) and environment criterion (e.g., energy consumption and carbon emission) are inevitable because of the requirement of practical production and life. In this article, we address this energy-efficient scheduling of distributed flow shop with heterogeneous factories for the first time, where contains permutation and hybrid flow shops. First, a new mathematical model of this problem with objectives of minimization makespan and total energy consumption is formulated. Then, a hybrid multiobjective optimization algorithm, which integrates the iterated greedy (IG) and an efficient local search, is designed to provide a set of tradeoff solutions for this problem. Furthermore, the parameter setting of the proposed algorithm is calibrated by using a Taguchi approach of design-of-experiment. Finally, to verify the effectiveness of the proposed algorithm, it is compared against other well-known multiobjective optimization algorithms including MOEA/D, NSGA-II, MMOIG, SPEA2, AdaW, and MO-LR in an automobile plant of China. Experimental results demonstrate that the proposed algorithm outperforms these six state-of-the-art multiobjective optimization algorithms in this real-world instance.
机译：凸轮轴加工的分布式流水车间调度是在汽车行业的一个重要的优化问题。分布式流水车间调度问题以前的研究主要强调同质工厂（车间类型是从工厂到工厂相同）和经济标准（例如，完工时间和拖期）。然而，异质工厂（店铺类型在不同的工厂变化）和环境条件（例如，能量消耗和碳排放）是因为实际生产和寿命的要求不可避免的。在这篇文章中，我们针对分布式流店的异构工厂这个节能调度的第一次，其中包含置换和混合流动商店。首先，这个问题最小化完工时间和总能耗的目标的一个新的数学模型制定。然后，混合多目标优化算法，它集成了迭代贪婪（IG）和高效的本地搜索，旨在提供一套针对此问题的折衷解决方案。此外，该参数所提出的算法的设置是通过使用设计的-实验的田口的方法进行校准。最后，为了验证该算法的有效性，这是对其他知名多目标优化算法，包括MOEA / d，NSGA-II，MMOIG，SPEA2，AdaW和MO-LR在中国的汽车厂相比。实验结果表明，该算法优于在这个真实世界的实例这六个国家的最先进的多目标优化算法。

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