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An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem

机译:改进的迭代贪婪算法和基于禁忌的重构策略,用于无等待流水车间调度问题

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This paper proposes a Tabu-mechanism improved iterated greedy (TMIIG) algorithm to solve the no-wait flowshop scheduling problem with a makespan criterion. The idea of seeking further improvement in the iterated greedy (IG) algorithm framework is based on the observation that the construction phase of the original IG algorithm may not achieve good performance in escaping from local minima when incorporating the insertion neighborhood search. To overcome this limitation, we have modified the IG algorithm by utilizing a Tabu-based reconstruction strategy to enhance its exploration ability. A powerful neighborhood search method that involves insert, swap, and double-insert moves is then applied to obtain better solutions from the reconstructed solution in the previous step. Empirical results on several benchmark problem instances and those generated randomly confirm the advantages of utilizing the new reconstruction scheme. In addition, our results also show that the proposed TMIIG algorithm is relatively more effective in minimizing the makespan than other existing well-performing heuristic algorithms. (C) 2015 Elsevier By. All rights reserved.
机译:提出了一种禁忌机制改进的迭代贪婪算法,以makepan为准则来解决无等待流水车间调度问题。在迭代贪婪(IG)算法框架中寻求进一步改进的想法是基于以下观察结果:在合并插入邻域搜索时,原始IG算法的构造阶段可能无法在逃避局部最小值方面获得良好的性能。为了克服此限制,我们通过利用基于禁忌的重建策略来改进IG算法,以增强其探索能力。然后,使用一种涉及插入,交换和双插入移动的功能强大的邻域搜索方法,从上一步中的重构解中获取更好的解。在一些基准问题实例和随机生成的实例上的经验结果证实了利用新的重建方案的优势。此外,我们的结果还表明,与其他现有的性能良好的启发式算法相比,所提出的TMIIG算法在最小化制造期方面相对更有效。 (C)2015年Elsevier By。版权所有。

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