Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe. By combining domain knowledge of the specific optimization problem, the search efficiency and quality of meta-heuristic algorithms can be significantly improved, making it crucial to identify and summarize domain knowledge within the problem. In this paper, we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem (JSP). Firstly, this paper delves into the importance of domain knowledge in optimization algorithm design. After that, the development of different methods for the JSP are reviewed, and the domain knowledge in it for meta-heuristic algorithms is summarized and classified. Applications of this domain knowledge are analyzed, showing it is indispensable in ensuring the optimization performance of meta-heuristic algorithms. Finally, this paper analyzes the relationship among domain knowledge, optimization problems, and optimization algorithms, and points out the shortcomings of the existing research and puts forward research prospects. This paper comprehensively summarizes the domain knowledge in the JSP, and discusses the relationship between the optimization problems, optimization algorithms and domain knowledge, which provides a research direction for the meta-heuristic algorithm design for solving the JSP in the future.

Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities, resulting in improved rationality of process routes and high-quality and efficient production. Hence, the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However, when performing this integrated optimization, the uncertainty of processing time is a realistic key point that cannot be neglected. Thus, this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem, FIPPS considers the fuzzy process time in the uncertain production environment, which is more practical and realistic. However, it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem, this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover, the critical path searching method is introduced according to the triangular fuzzy number, allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms, the results obtained by MSCOA show significant advantages, thus proving its effectiveness in solving the FIPPS problem.

Fault diagnosis plays the increasingly vital role to guarantee the machine reliability in the industrial enterprise. Among all the solutions, deep learning (DL) methods have achieved more popularity for their feature extraction ability from the raw historical data. However, the performance of DL relies on the huge amount of labeled data, as it is costly to obtain in the real world as the labeling process for data is usually tagged by hand. To obtain the good performance with limited labeled data, this research proposes a threshold-control generative adversarial network (TCGAN) method. Firstly, the 1D vibration signals are processed to be converted into 2D images, which are used as the input of TCGAN. Secondly, TCGAN would generate pseudo data which have the similar distribution with the limited labeled data. With pseudo data generation, the training dataset can be enlarged and the increase on the labeled data could further promote the performance of TCGAN on fault diagnosis. Thirdly, to mitigate the instability of the generated data, a threshold-control is presented to adjust the relationship between discriminator and generator dynamically and automatically. The proposed TCGAN is validated on the datasets from Case Western Reserve University and Self-Priming Centrifugal Pump. The prediction accuracies with limited labeled data have reached to 99.96% and 99.898%, which are even better than other methods tested under the whole labeled datasets.