  Priority dispatching rule (PDR) is widely used for solving real-world
Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a
tedious task, requiring a myriad of specialized knowledge and often delivering
limited performance. In this paper, we propose to automatically learn PDRs via
an end-to-end deep reinforcement learning agent. We exploit the disjunctive
graph representation of JSSP, and propose a Graph Neural Network based scheme
to embed the states encountered during solving. The resulting policy network is
size-agnostic, effectively enabling generalization on large-scale instances.
Experiments show that the agent can learn high-quality PDRs from scratch with
elementary raw features, and demonstrates strong performance against the best
existing PDRs. The learned policies also perform well on much larger instances
that are unseen in training.
