Iterative learning scheduling: a combination of optimization and dispatching rules

Dispatching rule‐based scheduling is a kind of dynamic scheduling commonly used in real world applications. Because of the lack of scheduling objective, it cannot optimize the specific performances at which shop managers aim in the current production period. To overcome the limitations of the dispatching rule‐based scheduling, an iterative learning scheduling scheme is proposed in this paper. A scheduling objective function, which reflects the performance criteria in which the shop managers are most interested, is established and used to guide the optimization of the crucial performances. According to the value of the scheduling objective obtained from the last simulation period, the parameters are adjusted so as to decrease the objective during the next simulation period. Experimental results show that the iterative learning scheduling overcomes the limitations of the dispatching rule‐based scheduling and achieves higher performances.