Bilevel learning for large-scale flexible flow shop scheduling

Li, Longkang; Fu, Xiujun; Zhen, Huiling; Yuan, Mingxuan; Wang, Jun; Lu, Jiawen; Tong, Xialiang; Zeng, Jia; Schnieders, Dirk

doi:10.1016/j.cie.2022.108140

Cited by 12 publications

(1 citation statement)

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“…Scheduling is a synthesized process of arranging, controlling, and optimizing tasks and workload in a manufacturing autonomous system, with broad applications in production and planning [1]- [4], regulatory designs [5]- [7], aviation and transportation [8]- [10]. In this paper, we utilize machine learning techniques to solve a frequently encountered and also versatile scheduling problem: the permutation flow shop scheduling (PFSS) problem [11]- [14], which is about sequentially processing several jobs on a series of machines.…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Liang²,

Zhu³

et al. 2022

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The permutation flow shop scheduling (PFSS), aiming at finding the optimal permutation of jobs, is widely used in manufacturing systems. When solving the 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 train the model via expert-driven imitation learning, which accelerates the 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-ofthe-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 source code is available at: https://github.com/longkang1/IL_PFSS

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