A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems

Dispatching rules are frequently used to schedule jobs in flexible manufacturing systems (FMSs) dynamically. A drawback, however, to using dispatching rules is that their performance is dependent on the state of the system, but no single rule exists that is superior to all the others for all the possible states the system might be in. This drawback would be eliminated if the best rule for each particular situation could be used. To do this, this paper presents a scheduling approach that employs machine learning. Using this latter technique, 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. Three different types of machine-learning algorithms will be used and compared in the paper to obtain ‘scheduling knowledge’: inductive learning, backpropagation neural networks, and case-based reasoning (CBR). A module that generates new control attributes allowing better identification of the manufacturing system's state at any particular moment in time is also designed in order to improve the ‘scheduling knowledge’ that is obtained. Simulation results indicate that the proposed approach produces significant performance improvements over existing dispatching rules.