Abstract Metaheuristic algorithms offer unique opportunities in problem solving. Although they do not guarantee optimality, it has been shown by numerous publications that they can achieve excellent results in acceptable time. Particularly in real-life production systems, which are mostly comprised of complex discrete optimization problems, the merit should be finding appropriate and efficient solutions in shorter periods rather than waiting for the optimum solution in whole shift. Accordingly, the present paper presents a learning iterated greedy search metaheuristic to minimize the maximum completion time in a hybrid flexible flowshop problem with sequence dependent setup times encountered at a manufacturing plant. The proposed algorithm is comprised of four main phases. The first phase employs NEH heuristic to generate an initial solution. Additionally, in order to introduce diversity, some replications are occasionally allowed to start with random solutions. Destruction mechanism to perturb the current solution is used in the next phase. It is followed by a construction procedure, which is used to repair the partial solution obtained after destruction. Finally, a descent neighborhood search enhanced by a hyper-heuristic based learning is applied to the repaired solution in the fourth phase. Thus, algorithm adaptively learns and promotes the most efficient low-level heuristic out of a heuristics pool and encourages the metaheuristic algorithm in using the promoted low-level heuristic in the final phase. The proposed algorithm along with its several extensions is tested by using real data taken from the mentioned production system. Next, by making use of the same data, the developed algorithms are compared to eight different algorithms, which are shown to be promising in the related literature. Finally, appropriate statistical tests are applied to demonstrate possible significant improvements among all tested algorithms.

中文翻译：

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基于超启发式学习增强迭代贪婪算法，解决具有序列相关设置时间的混合灵活流水车间调度问题：制造工厂的案例研究

摘要 元启发式算法为解决问题提供了独特的机会。尽管它们不能保证最优性，但许多出版物表明它们可以在可接受的时间内获得出色的结果。特别是在现实生活中的生产系统中，它主要由复杂的离散优化问题组成，优点应该是在更短的时间内找到合适和有效的解决方案，而不是在整个班次中等待最佳解决方案。因此，本论文提出了一种学习迭代贪婪搜索元启发式方法，以最小化混合柔性流水车间问题中的最大完成时间，该问题具有在制造工厂遇到的序列相关设置时间。所提出的算法由四个主要阶段组成。第一阶段使用 NEH 启发式生成初始解决方案。此外，为了引入多样性，有时允许一些复制从随机解决方案开始。在下一阶段使用破坏机制来扰乱当前的解决方案。接下来是一个构建程序，用于修复破坏后获得的部分解决方案。最后，通过基于超启发式学习增强的下降邻域搜索应用于第四阶段的修复解决方案。因此，算法自适应地学习和提升启发式池中最有效的低级启发式算法，并鼓励元启发式算法在最后阶段使用提升的低级启发式算法。通过使用从上述生产系统中获取的真实数据，对所提出的算法及其几个扩展进行了测试。接下来，通过使用相同的数据，将开发的算法与八种不同的算法进行比较，这些算法在相关文献中被证明是有前途的。最后，应用适当的统计测试来证明所有测试算法之间可能的显着改进。