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首页> 外文期刊>Arabian Journal for Science and Engineering >Earliness and Tardiness Minimizing on a Realistic Hybrid Flowshop Scheduling with Learning Effect by Advanced Metaheuristic
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Earliness and Tardiness Minimizing on a Realistic Hybrid Flowshop Scheduling with Learning Effect by Advanced Metaheuristic

机译：通过高级元启发式在具有学习效果的现实混合Flowshop调度中将提前和拖后时间降至最低

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摘要

This study proposed a novel hybrid metaheuristic that hybridized the particle swarm optimization, simulated annealing and variable neighborhood search to solve the hybrid flowshop scheduling with sequence-dependent setup times (SDST). To address the realistic assumptions of the proposed problem, three additional traits were added to the scheduling problem. These include SDST, position-dependent learning effects (LEs), and the consideration of tardiness together with earliness penalties as objective function. According to the best of our knowledge, this problem has never been investigated in the hybrid flowshop. Considering position-dependent LEs, it is assumed that the learning process reflects a decrease in the process and setup times as a function of the number of repetitions of the production of a same operation in a same stage because in many realistic situations, the more time you practice, the better LE you obtain. To evaluate the performance of the suggested method, the hybrid simulated annealing metaheuristic (HSA) presented recently is investigated for comparison purposes and computational experiments are performed on standard test problems. Results show that the suggested method performs better than the HSA for various test problems.
机译：这项研究提出了一种新颖的混合元启发式算法，该算法将粒子群优化，模拟退火和变量邻域搜索进行混合，以解决具有序列依赖的建立时间（SDST）的混合流水车间调度问题。为了解决提出的问题的现实假设，在调度问题中添加了三个其他特征。这些措施包括SDST，与位置有关的学习效果（LE），以及将迟到性与早期惩罚作为目标函数的考虑。据我们所知，此问题从未在混合流水车间中进行过调查。考虑到位置相关的LE，假设学习过程反映出过程和建立时间的减少是在同一阶段执行同一操作的重复次数的函数，因为在许多实际情况下，更多的时间练习，可以获得更好的LE。为了评估所建议方法的性能，研究了最近提出的混合模拟退火超启发式算法（HSA），以进行比较，并对标准测试问题进行了计算实验。结果表明，所提出的方法在各种测试问题上的性能均优于HSA。

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