A framework for dynamic rescheduling problems

Academic scheduling problems usually assume deterministic and known in advance data. However, this situation is not often met in practice, since data may be subject to uncertainty and it may change over time. In this paper, we introduce a general rescheduling framework to address such dynamic scheduling problems.

The framework consists mainly of a controller that makes use of a solver. The solver can assume deterministic and static data, whereas the controller deals with the uncertain and dynamic aspects of the problem and it is in charge of triggering the solver when needed and when possible. Extensive tests are carried out for the job shop problem, and we demonstrate that the framework can be used to ascertain the benefit of using rescheduling over static methods, decide between rescheduling policies, and finally we show that it can be applied in real-life applications due to a low time overhead.

The framework is general enough to be applied to any scheduling environment where a fast enough deterministic solver exists.