...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Production Research >A New Heuristic For The Flowshop Scheduling Problem To Minimize Makespan And Maximum Tardiness
【24h】

A New Heuristic For The Flowshop Scheduling Problem To Minimize Makespan And Maximum Tardiness

机译：Flowshop调度问题的新启发式方法，可最大程度地减少制造时间和最大延误

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

This paper presents a new heuristic for solving the flowshop scheduling problem that aims to minimize makespan and maximize tardiness. The algorithm is able to take into account the aforementioned performance measures, finding a set of non-dominated solutions representing the Pareto front. This method is based on the integration of two different techniques: a multi-criteria decision-making method and a constructive heuristic procedure developed for makespan minimization in flowshop scheduling problems. In particular, the technique for order preference by similarity of ideal solution (TOPSIS) algorithm is integrated with the Nawaz-Enscore-Ham (NEH) heuristic to generate a set of potential scheduling solutions. To assess the proposed heuristic's performance, comparison with the best performing multi-objective genetic local search (MOGLS) algorithm proposed in literature is carried out. The test is executed on a large number of random problems characterized by different numbers of machines and jobs. The results show that the new heuristic frequently exceeds the MOGLS results in terms of both non-dominated solutions, set quality and computational time. In particular, the improvement becomes more and more significant as the number of jobs in the problem increases.
机译：本文提出了一种新的启发式方法来解决Flowshop调度问题，该问题旨在最小化制造时间和最大程度地延缓拖延。该算法能够考虑上述性能指标，找到代表帕累托前沿的一组非支配解。此方法基于两种不同技术的集成：多准则决策方法和为在流水车间调度问题中最小化生产期而开发的建设性启发式程序。特别是，通过理想解决方案相似性（TOPSIS）算法进行订单偏好的技术与Nawaz-Enscore-Ham（NEH）启发式技术集成在一起，以生成一组潜在的调度解决方案。为了评估所提出的启发式算法的性能，与文献中提出的性能最好的多目标遗传局部搜索（MOGLS）算法进行了比较。该测试针对大量随机问题，这些随机问题的特征是机器和作业数量不同。结果表明，在非支配解，设置质量和计算时间方面，新的启发式方法经常超过MOGLS结果。尤其是，随着问题中的工作数量增加，改进变得越来越重要。

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