Learning-based scheduling in a job shop

Abstract

A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the test in all the possible states that the system may be in. The system's state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules.