The distributed reentrant permutation flow shop (DRPFS) is a combination of the reentrant flow shop problem and distributed scheduling. The DRPFS is a NP-hard problem that consists of two subproblems: (1) assigning a set of jobs to a set of available factories and (2) determining the operation sequence of jobs in each factory. This paper is the first study to consider the inclusion of sequence-dependent setup time in the DRPFS. The industrial applications of flow shop indicate that the machine setup time to process a job may depend on the previously processed jobs. Particularly, in DRPFS, the effect of sequence-dependent setup time is intensified due to its reentrant characteristic. An improved version of the multi-objective adaptive large neighborhood search (MOALNS) is proposed as a solution method for the sequence-dependent DRPFS with the aim to minimize the makespan, production cost, and tardiness. The proposed algorithm enhances the standard MOALNS by embedding an improved solution acceptance and non-dominated set updating criteria to assist the algorithm in finding the near-optimal Pareto front of the factory allocation and scheduling problems. To address the multiple objectives and the issue of non-uniform setup time, a new set of destroy and repair heuristics are developed. Further, the numerical experiments demonstrate the efficiency of IMOALNS in finding high-quality solutions in a relatively short time.

分布式重入置换流水车间（DRPFS）是重入流水车间问题和分布式调度的结合。DRPFS 是一个 NP-hard 问题，由两个子问题组成：（1）将一组作业分配给一组可用的工厂；（2）确定每个工厂中作业的操作顺序。本文是第一个考虑在 DRPFS 中包含依赖于序列的设置时间的研究。流水车间的工业应用表明，处理工作的机器设置时间可能取决于先前处理的工作。特别是，在 DRPFS 中，依赖于序列的设置时间的影响由于其可重入特性而加剧。提出了多目标自适应大邻域搜索 (MOALNS) 的改进版本作为序列相关 DRPFS 的解决方法，旨在最小化完工时间、生产成本和延迟。所提出的算法通过嵌入改进的解决方案接受和非支配集更新标准来增强标准 MOALNS，以帮助算法找到工厂分配和调度问题的近乎最优的帕累托前沿。为了解决多个目标和非统一设置时间的问题，开发了一组新的破坏和修复启发式方法。此外，数值实验证明了 IMOALNS 在相对较短的时间内找到高质量解的效率。所提出的算法通过嵌入改进的解决方案接受和非支配集更新标准来增强标准 MOALNS，以帮助算法找到工厂分配和调度问题的近乎最优的帕累托前沿。为了解决多个目标和非统一设置时间的问题，开发了一组新的破坏和修复启发式方法。此外，数值实验证明了 IMOALNS 在相对较短的时间内找到高质量解的效率。所提出的算法通过嵌入改进的解决方案接受和非支配集更新标准来增强标准 MOALNS，以帮助算法找到工厂分配和调度问题的近乎最优的帕累托前沿。为了解决多个目标和非统一设置时间的问题，开发了一组新的破坏和修复启发式方法。此外，数值实验证明了 IMOALNS 在相对较短的时间内找到高质量解的效率。