A genetic algorithm and data mining based meta-heuristic for job shop scheduling problem

Job shop scheduling (JSS) is a strongly NP-hard problem of combinatorial optimisation and one of the most well known machine scheduling problems. We propose a method based on a genetic algorithm and data mining to resolve this problem. The developed genetic algorithm generates a learning population of good solutions, which are mined by the mean of See5 classifier systems. The mining step produces decision rules which are transformed in to a meta-heuristic allowing the affectation of operations on machines.