Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to schedule job-shop problems: Representation and policy
  learning using graph neural network and reinforcement learning   We propose a framework to learn to schedule a job-shop problem (JSSP) using a
graph neural network (GNN) and reinforcement learning (RL). We formulate the
scheduling process of JSSP as a sequential decision-making problem with graph
representation of the state to consider the structure of JSSP. 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). We
employ Proximal Policy Optimization (PPO) based RL strategy to train these two
modules in an end-to-end fashion. We empirically demonstrate that the GNN
scheduler, due to its superb generalization capability, outperforms practically
favored dispatching rules and RL-based schedulers on various benchmark JSSP. We
also confirmed that the proposed framework learns a transferable scheduling
policy that can be employed to schedule a completely new JSSP (in terms of size
and parameters) without further training.

31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat