Intelligent dispatching in dynamic stochastic job shops

Dispatching rules are common method to schedule jobs in practice. However, they consider only limited factors which influence the priority of jobs. This limited consideration narrows the rules' scope of application. We develop a new hierarchical dispatching approach based on two types of factors: local factors and global factors, where each machine has its own dispatching rule setup. According to the global factors, the dispatchers divide the state of the manufacturing system into several patterns, and parameterize a neural network for each pattern to map the relationships between the local factors and the priorities of jobs. When making decisions, the dispatchers determine which pattern the current state belongs to. Then the appropriate neural network computes priorities according to the jobs' local factors. The job with the highest priority will be selected. Finally, the proposed approach is introduced on a manufacturing line and the performance is compared to classical dispatching rules.