Benchmark scheduling policies-MODFJSP

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.

为探究经超启发式协同进化生成的进化型调度策略（Scheduling Policies, SPs）的泛化性能，本研究从聚合帕累托前沿（Pareto front）中提取进化型SPs，并将其应用于64个测试场景，与320种现有人工组合调度策略展开对比；该类人工策略由32种工件排序规则与10种机器分配规则交叉组合而成。本数据集提供了进化型SPs与320种现有人工调度策略在多目标动态柔性作业车间调度问题（multi-objective dynamic flexible job shop scheduling problem）上的仿真性能数据。本研究采用实验设计（Design of Experiments, DOE）方法构建测试场景，选取6个各含两个水平的实验因子，共计生成64种因子组合，以此搭建测试集。