In semiconductor manufacturing we face many intricate scheduling problems. Simulation-based scheduling is a promising approach to deal with them. In conjunction with a metaheuristic we can solve many problem instances in a satisfactory manner. Nevertheless the quality of the results varies across the range of diverse challenges. Instead of performing extensive tests to determine the best metaheuristic and the optimal parameter setting for each case we propose the use of hyper-heuristics. A hyper-heuristic manages multiple metaheuristics to generate a solution for a broad field of applications. This paper will introduce two hyper-heuristics, one is an extended particle swarm algorithm the other is an integrated approach based on an evolutionary algorithm.
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