A REINFORCEMENT LEARNING ALGORITHM TO MINIMIZE THE MEAN TARDINESS OF A SINGLE MACHINE WITH CONTROLLED CAPACITY

摘要

In this work, we consider the problem of scheduling arriving jobs to a single machine where the objective is to minimize the mean tardiness. The scheduler has the option of reducing the processing time by half through the employment of an extra worker for an extra cost per job (setup cost). The scheduler can also choose from a number of dispatching rules. To find a good policy to be followed by the scheduler, we implemented a λ-SMART algorithm to do an on-line optimization for the studied system. The found policy is only optimal with respect to the state representation and set of actions available, however, we believe that the developed policies are easy to implement and would result in considerable savings as shown by the numerical experiments conducted.