Benchmark scheduling policies for MO-DFJSP

To investigate the generalization performance of the evolved scheduling policies(SPs), which are generated by the hyper-heuristic coevolution, the evolutionary SPs extracted from the aggerate Pareto front were applied to 64 testing scenarios to compare with the combinations of 320 existing man-made SPs which include 32 job sequencing rules and 10 machine assignment rules. This dataset provides the simulation performance of the evolved SPs and the 320 existing man-made SPs on the multi-objective dynamic flexible job shop scheduling problem. We applied a design of experiments (DOE) approach to design the testing scenarios, six experimental factors that each factor with two levels (including 64 combinations) are used to construct the test set.

本研究旨在探讨通过超启发式协同进化产生的调度策略（SP）的泛化性能。为此，我们从聚合帕累托前沿中提取的进化SP应用于64个测试场景，并与包含32项作业排序规则和10项机器分配规则的320个现有人工SP的组合进行对比。本数据集提供了进化SP和320个现有人工SP在多目标动态柔性作业车间调度问题上的模拟性能。我们采用实验设计（DOE）方法来设计测试场景，六个实验因素，每个因素包含两个水平（共64种组合）被用于构建测试集。