The permutation flow shop scheduling problem (PFSP), which is one of the most important scheduling types, is widespread in the modern industries. With the increase of scheduling scale, the difficulty and computation time of solving the problem will increase exponentially. Adding the knowledge to intelligent algorithms is a good way to solve the complex and difficult scheduling problems in reasonable time. To deal with the complex PFSPs, this paper proposes an improved simulated annealing (SA) algorithm based on residual network (SARes). First, this paper defines the neighborhood of the PFSP and divides its key blocks. Second, the Residual Network (ResNet) is used to extract and train the features of key blocks. And, the trained parameters are stored in the SA algorithm to improve its performance. Afterwards, some key operators, including the initial temperature setting and temperature attenuation function of SA algorithm, are also modified. After every new solution is generated, the parameters trained by the ResNet are used for fast ergodic search until the local optimal solution found in the current neighborhood. Finally, the most famous benchmarks including part of TA benchmark are selected to verify the performance of the proposed SARes algorithm, and the comparisons with the-state-of-art methods are also conducted. The experimental results show that the proposed method has achieved good results by comparing with other algorithms. This paper also conducts experiments on network structure design, algorithm parameter selection, CPU time and other problems, and verifies the advantages of SARes algorithm from the aspects of stability and efficiency.

排列流水车间调度问题（PFSP）是最重要的调度类型之一，已在现代工业中广泛使用。随着调度规模的增加，解决问题的难度和计算时间将成倍增加。将知识添加到智能算法中是一种在合理的时间内解决复杂而困难的调度问题的好方法。针对复杂的PFSP，提出了一种基于残差网络（SARes）的改进的模拟退火（SA）算法。首先，本文定义了PFSP的邻域并划分了其关键块。其次，残差网络（ResNet）用于提取和训练关键块的特征。并且，将训练后的参数存储在SA算法中以提高其性能。之后，一些主要的运营商 还修改了包括SA算法的初始温度设置和温度衰减功能在内的所有内容。生成每个新解决方案后，将使用ResNet训练的参数进行快速遍历搜索，直到在当前邻域中找到局部最优解决方案为止。最后，选择最著名的基准（包括TA基准的一部分）来验证所提出的SARes算法的性能，并与最新方法进行比较。实验结果表明，与其他算法相比，该方法取得了良好的效果。本文还对网络结构设计，算法参数选择，CPU时间等问题进行了实验，从稳定性和效率方面验证了SARes算法的优势。