Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning

TL;DR

This paper introduces a graph-based imitation learning approach for permutation flow shop scheduling, achieving faster convergence, higher accuracy, and better scalability than existing reinforcement learning methods.

Contribution

It proposes a novel graph-structured encoder and expert-driven imitation learning framework for PFSS, significantly improving solution quality and efficiency.

Findings

Achieves a solution gap reduction from 6.8% to 1.3%.

Reduces model parameters to 37% of previous methods.

Demonstrates strong generalization on large-scale problems with up to 1000 jobs.

Abstract

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}.