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.