  The permutation flow shop scheduling (PFSS), aiming at finding the optimal
permutation of jobs, is widely used in manufacturing systems. When solving
large-scale PFSS problems, traditional optimization algorithms such as
heuristics could hardly meet the demands of both solution accuracy and
computational efficiency, thus learning-based methods have recently garnered
more attention. Some work attempts to solve the problems by reinforcement
learning methods, which suffer from slow convergence issues during training and
are still not accurate enough regarding the solutions. To that end, we propose
to train the model via expert-driven imitation learning, which accelerates
convergence more stably and accurately. Moreover, in order to extract better
feature representations of input jobs, we incorporate the graph structure as
the encoder. The extensive experiments reveal that our proposed model obtains
significant promotion and presents excellent generalizability in large-scale
problems with up to 1000 jobs. Compared to the state-of-the-art reinforcement
learning method, our model's network parameters are reduced to only 37\% of
theirs, and the solution gap of our model towards the expert solutions
decreases from 6.8\% to 1.3\% on average. The code is available at:
\url{https://github.com/longkangli/PFSS-IL}.
