For solving the job-shop scheduling problem (JSP), this paper proposes a novel two-level metaheuristic algorithm, where its upper-level algorithm controls the input parameters of its lower-level algorithm. The lower-level algorithm is a local search algorithm searching for an optimal JSP solution within a hybrid neighborhood structure. To generate each neighbor solution, the lower-level algorithm randomly uses one of two neighbor operators by a given probability. The upper-level algorithm is a population-based search algorithm developed for controlling the five input parameters of the lower-level algorithm, i.e., a perturbation operator, a scheduling direction, an ordered pair of two neighbor operators, a probability of selecting a neighbor operator, and a start solution-representing permutation. Many operators are proposed in this paper as options for the perturbation and neighbor operators. Under the control of the upper-level algorithm, the lower-level algorithm can be evolved in its input-parameter values and neighborhood structure. Moreover, with the perturbation operator and the start solution-representing permutation controlled, the two-level metaheuristic algorithm performs like a multistart iterated local search algorithm. The experiment’s results indicated that the two-level metaheuristic algorithm outperformed its previous variant and the two other high-performing algorithms in terms of solution quality.

中文翻译：

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车间作业调度问题的自适应混合邻域结构增强两元元启发式算法

为了解决作业车间调度问题（JSP），提出了一种新颖的两级元启发式算法，该算法的上级算法控制下级算法的输入参数。下层算法是在混合邻域结构内搜索最佳JSP解决方案的本地搜索算法。为了生成每个邻居解，较低级算法以给定概率随机使用两个邻居运算符之一。上层算法是基于种群的搜索算法，用于控制下层算法的五个输入参数，即扰动算子，调度方向，两个邻居算子的有序对，选择邻居的概率运算符，以及代表序列的起始解。本文提出了许多算子作为扰动算子和邻居算子的选择。在上级算法的控制下，下级算法可以在其输入参数值和邻域结构上发展。此外，在控制了扰动算子和表示代表置换的起始解的情况下，两级元启发式算法的性能类似于多起始迭代局部搜索算法。实验结果表明，在求解质量方面，两级元启发式算法的性能优于其先前的变体，而其他两个高性能算法则优于其他两种。在摄动算子和起始解表示代表置换的控制下，两级元启发式算法的性能类似于多起始迭代局部搜索算法。实验结果表明，在求解质量方面，两级元启发式算法的性能优于其先前的变体，而其他两个高性能算法则优于其他两种。在摄动算子和起始解表示代表置换的控制下，两级元启发式算法的性能类似于多起始迭代局部搜索算法。实验结果表明，在求解质量方面，两级元启发式算法的性能优于其先前的变体，而其他两个高性能算法则优于其他两种。