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Efficient heuristics for flowshop sequencing with the objectives of makespan and flowtime minimisation

机译:针对流水车间定序的高效启发式方法,目标是最小化制造时间和流程时间

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摘要

In this paper we analyse the performance of flowshop sequencing heuristics with respect to the objectives of makespan and flowtime minimisation. For flowtime minimisation, we propose the strategy employed by the NEH heuristic to construct partial solutions. Results show that this approach outperforms the common fast heuristics for flowtime minimisation while performing similarly or slightly worse than others which, on reward, prove to be much more CPU time-consuming. Additionally, the suggested approach is well balanced with respect to makespan and flowtime minimisation. Based on the previous results, two algorithms are proposed for the sequencing problem with multiple objectives - makespan and flowtime minimisation. These algorithms provide the decision maker with a set of heuristically efficient solutions such that he/she may choose the most suitable sequence for a given ratio between costs associated with makespan and those assigned to flowtime. Computational experience shows both algorithms to perform better than the current heuristics designed for the two-criteria problem.
机译:在本文中,我们针对制造期和最小化流动时间的目标分析了Flowshop排序试探法的性能。为了使流程时间最小化,我们提出了NEH启发式算法采用的策略来构造部分解决方案。结果表明,这种方法在最小化流程时间方面优于常用的快速启发式方法,同时其性能与其他方法类似或稍差,后者值得庆幸的是,这更耗时。此外,建议的方法在最小化制造时间和缩短流动时间方面取得了很好的平衡。基于先前的结果,针对具有多个目标的排序问题,提出了两种算法-最小化制造时间和最小化流动时间。这些算法为决策者提供了一组启发式有效的解决方案,以使他/她可以为与工期相关的成本与分配给流动时间的成本之间的给定比率选择最合适的顺序。计算经验表明,这两种算法的性能都优于针对两准则问题而设计的当前启发式算法。

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