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

机译:使用MakeSpan标准的Noidle Flowshop调度问题的一般变量邻域搜索问题

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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:本文提出了一种新颖的变量邻域搜索(GVNS)算法来解决MakeSpan标准的No-Idle Flowshop调度问题。使用FRB5启发式生成GVN的初始解决方案。在外循环中,采用插入和交换操作来摇动置换。在可变邻域下降过程的内圈中,使用两个有效算法,即迭代贪婪(IG)和可变块插入启发式(VBIH)算法。注意,在这些IG和VBIH算法中采用有效的参考插入方案。使用基准实例将所提出的GVNS算法与标准IG算法进行比较。计算实验表明,GVN比标准IG更好。此外,将标准IG和GVNS算法的结果与文献中报道的当前最着名的解决方案进行了比较。计算结果表明,所提出的GVNS算法改善了文献中最新的最佳已知解决方案。因此,可以说,GVNS对MakeSpan标准的No-Idle Flowshop调度问题非常有效。

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