Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems

We propose a two-stage hyper-heuristic for the generation of a set of work centre-specific dispatching rules. The approach combines a genetic programming (GP) algorithm that evolves a composite rule from basic job attributes with an evolutionary algorithm (EA) that searches for a good assignment of rules to work centres. The hyper-heuristic is tested against its two components and rules from the literature on a complex dynamic job shop problem from semiconductor manufacturing. Results show that all three hyper-heuristics are able to generate (sets of) rules that achieve a significantly lower mean weighted tardiness than any of the benckmark rules. Moreover, the two-stage approach proves to outperform the GP and EA hyper-heuristic as it optimises on two different heuristic search spaces that appear to tap different optimisation potentials. The resulting rule sets are also robust to most changes in the operating conditions.