...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on automation science and engineering >A Pareto-Based Estimation of Distribution Algorithm for Solving Multiobjective Distributed No-Wait Flow-Shop Scheduling Problem With Sequence-Dependent Setup Time
【24h】

A Pareto-Based Estimation of Distribution Algorithm for Solving Multiobjective Distributed No-Wait Flow-Shop Scheduling Problem With Sequence-Dependent Setup Time

机译：求解序列依赖建立时间的多目标分布式无等待流水车间调度问题的基于帕累托的分布算法估计

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

Influenced by the economic globalization, the distributed manufacturing has been a common production mode. This paper considers a multiobjective distributed no-wait flowshop scheduling problem with sequence-dependent setup time (MDNWFSP-SDST). This scheduling problem exists in many real productions such as baker production, parallel computer system, and surgery scheduling. The performance criteria are the makespan and the total weight tardiness. In the MDNWFSP-SDST, several identical factories are considered with the related flow-shop scheduling problem with no-wait constraints. For solving the MDNWFSP-SDST, a Pareto-based estimation of distribution algorithm (PEDA) is presented. Three probabilistic models including the probability of jobs in empty factory, two jobs in the same factory, and the adjacent jobs are constructed. The PWQ heuristic is extended to the distributed environment to generate initial individuals. A sampling method with the referenced template is presented to generate offspring individuals. Several multiobjective neighborhood search methods are developed to optimize the quality of solutions. The comparison results show that the PEDA obviously outperforms other considered multiobjective optimization algorithms for addressing MDNWFSP-SDST.Note to Practitioners-This paper is motivated by the process cycles in multiproduction factories (or lines) of baker production, surgery scheduling, and parallel computer systems. In these process cycles, jobs are assigned to multiproduction factories (or lines), and no interruption exists between consecutive operations. This paper models this process as a multiobjective distributed no-wait flow-shop scheduling with SDST. Scheduling becomes more challenging when facing distributed factories. This paper provides an estimation of distributed algorithm with Pareto dominate concept which uses a probabilistic model to generate offspring. Experiment results suggest that the proposed algorithm can find superior solutions of large-scale instances. This scheduling model can be extended to practical problems by considering other constraints, such as assembly process, mixed no-wait, and transporting times. Besides, the proposed algorithm can be applied to solve other distributed scheduling problems and industrial cases, once their constraints are known, i. e., the processing time of operations, the setup time of machines.
机译：在经济全球化的影响下，分布式制造已成为一种普遍的生产方式。本文考虑了与序列相关的建立时间（MDNWFSP-SDST）的多目标分布式无等待流水车间调度问题。这种调度问题存在于许多实际产品中，例如面包师生产，并行计算机系统和手术调度。性能标准是制造期和总重迟滞。在MDNWFSP-SDST中，考虑了几个完全相同的工厂，并带有相关的无等待约束的流水车间调度问题。为了解决MDNWFSP-SDST，提出了一种基于帕累托的分布估计算法（PEDA）。构建了三个概率模型，包括空工厂中的工作概率，同一工厂中的两个工作以及相邻工作的概率。 PWQ启发式方法已扩展到分布式环境以生成初始个体。提出了具有参考模板的抽样方法以产生后代个体。开发了几种多目标邻域搜索方法来优化解决方案的质量。比较结果表明，在解决MDNWFSP-SDST问题上，PEDA明显优于其他考虑的多目标优化算法。从业者的注意-本文受制于面包生产，手术调度和并行计算机系统的多生产工厂（或生产线）中的生产周期的启发。 。在这些过程周期中，将作业分配给多生产工厂（或生产线），并且连续操作之间不存在中断。本文将此过程建模为具有SDST的多目标分布式无等待流水车间调度。面对分布式工厂时，调度变得更具挑战性。本文提供了一种基于帕累托控制的分布式算法的估计，该算法使用概率模型生成后代。实验结果表明，该算法可以找到大规模实例的最优解。通过考虑其他约束，例如组装过程，混合等待时间和运输时间，可以将该调度模型扩展到实际问题。此外，一旦知道了它们的约束条件，该算法就可以用于解决其他分布式调度问题和工业案例。例如，操作的处理时间，机器的设置时间。

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