Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning

no code implementations • 2 Jun 2021 • Junyoung Park, Jaehyeong Chun, Sang Hun Kim, Youngkook Kim, Jinkyoo Park

In solving the formulated problem, the proposed framework employs a GNN to learn that node features that embed the spatial structure of the JSSP represented as a graph (representation learning) and derive the optimum scheduling policy that maps the embedded node features to the best scheduling action (policy learning).

Decision Making Graph Representation Learning +2