Dynamic scheduling of flexible manufacturing systems using neural networks and inductive learning

Dispatching rules are usually applied dynamically to schedule jobs in flexible manufacturing systems. Despite their frequent use, one of the drawbacks that they display is that the state the manufacturing system is in dictates the level of performance of the rule. As no rule is better than all the other rules for all system states, it would be highly desirable to know which rule is the most appropriate for each given condition, and to this end this paper proposes a scheduling approach that employs inductive learning and backpropagation neural networks. Using these latter techniques, and by analysing the earlier performance of the system, “scheduling knowledge” is obtained whereby the right dispatching rule at each particular moment can be determined. A module that generates new control attributes is also designed in order to improve the “scheduling knowledge” that is obtained. Simulation results show that the proposed approach leads to significant performance improvements over existing dispatching rules.