  The interrupting swap-allowed blocking job shop problem (ISBJSSP) is a
complex scheduling problem that is able to model many manufacturing planning
and logistics applications realistically by addressing both the lack of storage
capacity and unforeseen production interruptions. Subjected to random
disruptions due to machine malfunction or maintenance, industry production
settings often choose to adopt dispatching rules to enable adaptive, real-time
re-scheduling, rather than traditional methods that require costly
re-computation on the new configuration every time the problem condition
changes dynamically. To generate dispatching rules for the ISBJSSP problem, we
introduce a dynamic disjunctive graph formulation characterized by nodes and
edges subjected to continuous deletions and additions. This formulation enables
the training of an adaptive scheduler utilizing graph neural networks and
reinforcement learning. Furthermore, a simulator is developed to simulate
interruption, swapping, and blocking in the ISBJSSP setting. Employing a set of
reported benchmark instances, we conduct a detailed experimental study on
ISBJSSP instances with a range of machine shutdown probabilities to show that
the scheduling policies generated can outperform or are at least as competitive
as existing dispatching rules with predetermined priority. This study shows
that the ISBJSSP, which requires real-time adaptive solutions, can be scheduled
efficiently with the proposed method when production interruptions occur with
random machine shutdowns.
