Benchmark scheduling policies for MO-DFJSP

Citation Author(s):
Yong Zhou
Submitted by:
Yong Zhou
Last updated:
Tue, 05/17/2022 - 22:17
DOI:
10.21227/0ja1-fx80
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Abstract 

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.

Instructions: 

Using software jasima,which can be found at https://bitbucket.org/jasimaSolutions/jasima, the combinations of 320 existing man-made scheduling policies are applied to 64 testing training scenarios (more details shown below), and 100 simulation replications are performed for each scenario.

Simulation Parameters
Weights of jobs 4:2:1(20%:60%:20%)
Simulation Length 2500
Warmup Length 500
Machine number 10
Operation number distribution Missing, Full
Process time distribution U[1,99], N(120,20)
Allowance factor 1, 3
Utilization 80%, 95%
Optional device number U[1,2], U[1,4]