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-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.

翻译：旨在寻找最佳工作更替的变异流动商店时间安排(PFSS)在制造系统中被广泛使用。在解决大规模PFSS问题时,传统优化算法,如湿质学等传统优化算法,很难满足解决方案准确性和计算效率的要求。因此,以学习为基础的方法最近得到更多的关注。一些通过强化学习方法来解决问题的努力,这些方法在培训期间遇到缓慢的趋同问题,而且对于解决办法仍然不够准确。为此,我们通过专家驱动的模拟学习来培训模型,从而更精确地加速趋同。此外,为了更好地体现投入工作的特点,我们把图形结构作为编码器。广泛的实验表明,我们提议的模型获得了显著的推广,并展示出在1,000个工作岗位的大规模问题中极具普遍性。与最先进的强化学习方法相比,我们模型的网络参数只减少到37个,而我们模型对专家解决办法的解决方案差距平均从6.8个减少到1.3个。