Developing dispatching rules for manufacturing systems is a tedious process, which is time-and cost-consuming. Since there is no good general rule for different scenarios and objectives automatic rule search mechanism are investigated. In this paper an approach using Genetic Programming (GP) is presented. The priority rules generated by GP are evaluated on dynamic job shop scenarios from literature and compared with manually developed rules yielding very promising results also interesting for Simulation Optimization in general.

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Dispatching rules play an important role especially in semiconductor manufacturing scheduling, because these fabrication facilities are characterized by high complexity and dynamics. The process of developing and adapting dispatching rules is currently a tedious, largely manual task. Coupling Genetic Programming (GP), a global optimization meta-heuristic from the family of Evolutionary Algorithms, with a stochastic discrete event simulation of a complex manufacturing system we are able to automatically generate dispatching rules for a scenario from semiconductor manufacturing. Evolved dispatching rules clearly outperform manually developed rules from literature.

Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence dependent setup times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs.

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