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A General Variable Neighborhood Search for the NoIdle Flowshop Scheduling Problem with Makespan Criterion

机译:带有Makespan准则的NoIdle Flowshop调度问题的通用变量邻域搜索

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This paper proposes a novel general variable neighborhood search (GVNS) algorithm to solve the no-idle flowshop scheduling problem with the makespan criterion. The initial solution of the GVNS is generated using the FRB5 heuristic. In the outer loop, insert and swap operations are employed to shake the permutation. In the inner loop of variable neighborhood descent procedure, two effective algorithms, namely, Iterated Greedy (IG) and Variable Block Insertion Heuristic (VBIH) algorithms are used. Note that, an effective referenced insertion scheme is employed in these IG and VBIH algorithms. The proposed GVNS algorithm is compared with the standard IG algorithm using the benchmark instances. The computational experiments show that the GVNS performs much better than the standard IG. Furthermore, the results of the standard IG and GVNS algorithms are compared with the current best-known solutions reported in the literature. The computational results show that the proposed GVNS algorithm improves some of the current best-known solutions in the literature. Consequently, it can be said that the GVNS is very effective for the no-idle flowshop scheduling problem with the makespan criterion.
机译:提出了一种新颖的通用变量邻域搜索(GVNS)算法,以makepan标准解决无空闲流水车间调度问题。 GVNS的初始解决方案是使用FRB5启发式方法生成的。在外部循环中,使用插入和交换操作来摇晃排列。在可变邻域下降过程的内部循环中,使用了两种有效算法,即迭代贪婪(IG)和可变块插入启发式(VBIH)算法。请注意,在这些IG和VBIH算法中采用了有效的参考插入方案。使用基准实例将提出的GVNS算法与标准IG算法进行比较。计算实验表明,GVNS的性能比标准IG好得多。此外,将标准IG和GVNS算法的结果与文献中报道的当前最著名的解决方案进行了比较。计算结果表明,所提出的GVNS算法改进了文献中当前一些最著名的解决方案。因此,可以说GVNS对于具有制造期标准的无空流水车间调度问题非常有效。

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