The distributed no-idle permutation flowshop scheduling problem with due windows algorithms

"The Distributed No-Idle Flowshop Scheduling Problem with Due Windows (DNIFSPDW)" addresses an extension of the Distributed Permutation Flowshop Scheduling Problem with No-Idle and Due Window constraints. The objective of DNIFSPDW is to determine the optimal sequence of job assignments to factories and the sequence in which they should be performed in each factory. This optimal sequence should ensure the minimum total weighted earliness and tardiness (TWET) penalties while also taking into account the due windows. The inclusion of a total weighted tardiness objective in the flowshop scheduling problem (FSP), a known NP-hard issue, means that the DNIPFSPDW also inherits this computational complexity, classifying it under the NP-hard category. As a result, exact solution methods are not efficient for large-scale instances of this problem. For such intricate challenges, metaheuristic approaches are more appropriate as they can achieve high-quality solutions in reasonable computation times. Notably, the iterated greedy metaheuristic has proven effective for most PFSPs. Therefore, two hybrid iterated greedy algorithms namely hybrid iterated greedy-tabu search and hybrid iterated greedy-local search are developed for the DNIPFSPDW. The two attached documents contain the codes for the algorithms discussed. Each file includes the data developed and used to evaluate the distributed no-idle flowshop scheduling problem with due windows. For detailed instructions on executing these codes, please refer to the enclosed Readme file. The document titled "Java Codes" houses the executable codes.带交货期窗口的分布式无空闲流水车间调度问题（Distributed No-Idle Flowshop Scheduling Problem with Due Windows，DNIFSPDW）针对带无空闲约束与交货期窗口约束的分布式置换流水车间调度问题进行了拓展。该问题的优化目标为确定工件分配至各工厂的最优顺序，以及各工厂内部的工件加工顺序，该最优顺序需在兼顾交货期窗口的前提下，实现总加权提前与拖期惩罚（total weighted earliness and tardiness，TWET）的最小化。流水车间调度问题（Flowshop Scheduling Problem，FSP）的总加权拖期优化目标本身已是公认的NP难问题，因此DNIFSPDW同样继承了该计算复杂性，属于NP难问题范畴。故而精确求解方法对于该问题的大规模实例而言效率低下。针对这类复杂挑战，元启发式算法更为适用，其可在合理的计算时间内获得高质量解。值得注意的是，迭代贪婪（Iterated Greedy）元启发式算法已被证实对多数置换流水车间调度问题有效。因此，本文针对DNIFSPDW提出了两种混合迭代贪婪算法：混合迭代贪婪-禁忌搜索（Tabu Search）算法与混合迭代贪婪-局部搜索（Local Search）算法。 所附的两份文档包含了上述算法的代码，每份文件均涵盖了用于评估带交货期窗口的分布式无空闲流水车间调度问题的数据集。如需获取代码运行的详细说明，请参阅随附的Readme文件。标题为"Java Codes"的文档包含可执行代码。