In the industrial field, malfunction of rotating machinery, especially bearings, can cause significant economic losses to enterprises. Addressing the limitations of traditional fault diagnosis methods, such as poor generalization performance and low noise resistance, this paper introduces a fault diagnosis model that parallels the cross convolutional transformer and ResNet18 (CCTAR). The proposed CCTAR utilizes two feature extraction channels, aimed at balancing the extraction of local and global features, and the specially designed convolutional cross-decoding layer has excellent noise resistance, surpassing traditional multi-layer Transformer encoding layers with a single-layer structure. CCTAR achieves commendable recognition accuracy across multiple datasets and maintains high accuracy in noisy environments. Furthermore, transfer learning experiments have demonstrated the proposed model’s capability to achieve superior fault diagnosis performance across different working conditions with a limited number of samples, highlighting its practical significance.

Shuttle-based storage and retrieval systems (SBS/RSs), a new type of “part-to-picker” system, have attracted public attention because of their flexibility and robustness. The systems consist of two sub-systems—shuttles and lifts—which are responsible for horizontal movements and vertical movements, respectively. SBS/RSs are categorized into tier-to-tier shuttles and tier-captive shuttles. This paper mainly researches SBS/RSs with tier-to-tier shuttles. New challenges have emerged since the commercial simulation-based optimization software has become widely applied for, for instance, task scheduling problems. Specifically, to decrease the idle time of lifts and the waiting time of shuttles, an integer programming scheduling model is proposed to minimize total task time with constraints on orders and devices. The Gurobi linear programming solver was utilized to obtain the optimal order sequence and the shortest operation time. The overall system efficiency was improved using our model. Moreover, the results are significant for reducing capital investment and operating cost.

With the development of the social economy and the improvement of the consumption concept, a new business model combining offline and online has been promoted. The warehousing system is one of the important links of commodity production and circulation, which involves storage, sorting, and distribution. It has a significant impact on the operation cost and the efficiency of the whole logistics system. The progress of robot technology, the Internet of things, and artificial intelligence technology promotes the automation and intelligence of storage systems. The Robotic Mobile Fulfillment Systems (RMFS), which takes the automatic guided vehicles (AGVs) as the way of handling and picking, greatly improves the space utilization, operation efficiency, and flexibility of the system. This paper studies the RMFS with fixed shelves and establishes the performance evaluation model of the picking system considering the AGVs congestion by establishing the queuing network. The effectiveness of the model is verified by simulation, and the optimization of system parameter configuration is further discussed according to the experimental data.

Robotic Mobile Fulfillment System (RMFS) affects the traditional scheduling problems heavily while operating a warehouse. This paper focuses on storage assignment optimization for Fishbone Robotic Mobile Fulfilment Systems (FRMFS). Based on analyzing operation characteristics of FRMFS, a storage assignment optimization model is proposed with the objectives of maximizing operation efficiency and balancing aisle workload. Adaptive Genetic Algorithm (AGA) is designed to solve the proposed model. To validate the effectiveness of AGA in terms of iteration and optimization rate, this paper designs a variety of scenarios with different task sizes and storage cells. AGA outperforms other four algorithm in terms of fitness value and convergence and has better convergence rate and stability. The experimental results also show the advancement of AGA in large size FRMFS. In conclusion, this paper proposes a storage assignment model for FRMFS to reduce goods movement and travel distance and improve the order picking efficiency.

The shuttle-based storage and retrieval system (SBS/RS) is composed of a shuttle sub-system that is responsible for horizontal movements and a lift sub-system that is responsible for vertical movements. As the combination of the two sub-systems yields high flexibility, low operating cost and large storage capacity, SBS/RS is becoming more and more popular, but also raises managerial issue of how to coordinate the shuttle and the lift. Based on its operational processes, this study models the tier-to-tier SBS/RS system as a semi-open queuing network (SOQN). By removing or adding the synchronization nodes, the SOQN is further transformed into two different closed queuing networks (CQNs), and applies the approximate mean value analysis (AMVA) algorithm to solve the model and estimate its performance. The system performance is measured by the utilization of shuttles, the utilization of lift, and the task cycle time, under various design configurations. Simulation is carried out to validate the effectiveness of the analytical model and algorithm. Compared with the simulation results, the established semi-open queuing network can accurately estimate task cycle time for different rack configurations. The proposed solution method can help to identify the optimal number of shuttles and guide the design of the SBS/RS system.

The application of scene recognition in intelligent robots to forklift AGV equipment is of great significance in order to improve the automation and intelligence level of distribution centers. At present, using the camera to collect image information to obtain environmental information can break through the limitation of traditional guideway and positioning equipment, and is beneficial to the path planning and system expansion in the later stage of warehouse construction. Taking the forklift AGV equipment in the distribution center as the research object, this paper explores the scene recognition and path planning of forklift AGV equipment based on a deep convolution neural network. On the basis of the characteristics of the warehouse environment, a semantic segmentation network applied to the scene recognition of the warehouse environment is established, and a scene recognition method suitable for the warehouse environment is proposed, so that the equipment can use the deep learning method to learn the environment features and achieve accurate recognition in the large-scale environment, without adding environmental landmarks, which provides an effective convolution neural network model for the scene recognition of forklift AGV equipment in the warehouse environment. The activation function layer of the model is studied by using the activation function with better gradient performance. The results show that the performance of the H-Swish activation function is better than that of the ReLU function in recognition accuracy and computational complexity, and it can save costs as a calculation form of the mobile terminal.