The distributed flexible job shop scheduling problem (DFJSP) has emerged as a critical challenge in the field of scheduling optimization due to its intricate resource allocation and the demand for production–logistics collaboration across multiple factories. However, most existing studies related to DFJSP only focus on the production and transportation process of jobs within a single factory, while neglecting the cross-factory logistics and the heterogeneous characteristics of transportation resources. Therefore, this article first investigates the distributed flexible job shop scheduling problem with heterogeneous transportation (DFJSPHT) resource constraints and proposes an end-to-end deep reinforcement learning (DRL) scheduling method to minimize the makespan. An innovative heterogeneous disjunctive graph model is constructed to uniformly represent the states of factories, machines, operations, and transportation resources in DFJSPHT, and the scheduling process is modeled as a Markov decision process (MDP). Next, a resource release strategy is developed to enhance the efficiency of transportation resources. To enhance the feature expression ability of the model, a graph neural network (GNN) is employed to capture the problem characteristics, and the policy network is trained using the proximal policy optimization. Comparative experiments are conducted on synthetic and benchmark instances demonstrate that the proposed method outperforms the classical priority scheduling rules and two popular DRL-based scheduling methods in solving DFJSPHT, with performance improvements exceeding 10% in most instances.

In intelligent manufacturing systems, accurate and timely fault diagnosis is crucial for ensuring a safe and stable manufacturing process. While transfer learning (TL) can mitigate the need for extensive labeled data, not all historical datasets are applicable to specific fault diagnosis tasks, and the use of inappropriate datasets can deteriorate the accuracy of TL models. To address these issues, a TL fault diagnosis method based on cloud-edge collaboration is proposed. First, a variational autoencoder TL algorithm based on multiscale convolution and domain fusion (MSDF-VAE) is presented to effectively leverage extensive historical fault data, particularly in scenarios with limited labeled samples. Second, a lightweight autoencoder model (LAE) is employed to improve the reusability and specificity of historical data and fault diagnosis models by analyzing the correlation between historical data and the current data. Additionally, to reduce latency and meet real-time requirements for fault diagnosis tasks, a cloud-edge collaborative framework is proposed, within which MSDF-VAE and LAE are deployed. This approach enables real-time diagnosis using the MSDF-VAE model at the edge layer, while the cloud layer concurrently trains a high-precision model with the selected data by the LAE. The experiments verify the accuracy of the MSDF-VAE and confirm the effectiveness of the proposed cloud-edge collaboration framework.

Industrial robots (IRs) have considerable energy-saving potential due to their vast application scale and wide range of applications. Although substantial work on the energy consumption (EC) optimization of IRs has emerged, most optimization approaches require prior knowledge of the IRs' dynamic characteristics and the electro-mechanical parameters of their drive systems, which are typically not provided by IR manufacturers. Therefore, this article proposes an EC modeling and optimization method based on the time-scaling technique and custom identification experimental data without joint torque information. Specifically, this article develops an energy characteristic parameter submodel (ECPSM) to formulate the EC resulting from configuration transitions. In addition, theoretical proof demonstrates that all coefficients in the proposed ECPSM can be identified based on the data of a finite number of identification experiments. Building upon the proposed EC model, a bidirectional dynamic programming (BDP) algorithm optimizes the IR's trajectory for energy-saving, while utilizing parallel processing significantly reduces the time required for the optimization process. Experimental results on the KUKA KR60-3 demonstrate that the proposed method achieves an average relative error of 1.59% for predicting the EC of linear scaling trajectories and 6.19% for nonlinear scaled trajectories. Moreover, the BDP-based optimization method dramatically reduces the computational time required to obtain the optimal scaling trajectory and its EC.