The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases.

Low-carbon sustainable development has become the consensus of manufacturing enterprises to fulfill their social responsibilities. Facility layout is an essential part of manufacturing system planning. Current research has demonstrated the advantages of energy saving on the manufacturing system level where operational methods (e.g., energy-efficient production scheduling and path planning) can be utilized and do not require massive investment in the existing legacy system. However, these efforts are mostly based on the existing fixed facility layout. Meanwhile, although facility layout problems have been extensively studied so far, the related work seldom involves the optimization of energy consumption (EC) or other EC-related environmental impact indicators, and does not clearly reveal if EC can be an independent optimization objective in facility layout. Accordingly, whether the energy-saving potential of a manufacturing system can be further tapped through rational facility layout is the gap of the current study. To address this, an investigation into energy-saving oriented manufacturing workshop facility layout is conducted. Correspondingly, an energy-efficient facility layout (EFL) model for the multi-objective optimization problem that minimizes total load transport distance and EC is formulated, and a multi-objective particle swarm optimization-based method is proposed as the solution. Furthermore, experimental studies verify the effectiveness of the presented model and its solution, indicating that EC can be regarded as an independent optimization objective during facility layout, and EFL is a feasible energy-saving approach for a manufacturing system.

The four-way-shuttle-based storage and retrieval system is a recent innovative intelligent vertical warehousing system that has been widely applied in manufacturing and e-commerce environments due to its high flexibility and density. As a complex multi-device cooperative operational system, this system features the parallel operation of multiple elevators and four-way shuttles. During large-scale-batch inbound operations, the quality of scheduling solutions for inbound-operation equipment significantly impacts the system’s efficiency and performance. In this paper, a detailed analysis of the inbound-operation process in the system is conducted, taking into consideration the motion characteristics of both the elevators and four-way shuttles. Furthermore, we establish operational time constraints that account for equipment acceleration and deceleration characteristics and introduce a flexible flow-shop-scheduling model to address the scheduling problem in the system. Additionally, we propose an improved genetic algorithm based on double-layer encoding to solve this problem. Comparative experiments with a traditional genetic algorithm and ant-colony algorithm demonstrate the superior efficiency and accuracy of our approach. Finally, the effectiveness of the proposed algorithm is validated through comparisons with large-scale practical experiments.

Accurate and real-time detection of rice shelling rate (SR) and brown rice breakage rate (BR) is crucial for intelligent hulling sorting but remains challenging because of small grain size, dense adhesion, and uneven illumination causing missed detections and blurred boundaries in traditional YOLOv8n. This paper proposes a high-precision, lightweight solution based on an enhanced YOLOv8n with improvements in network architecture, feature fusion, and attention mechanism. The backbone’s C2f module is replaced with C2f-Faster-CGLU, integrating partial convolution (PConv) local convolution and convolutional gated linear unit (CGLU) gating to reduce computational redundancy via sparse interaction and enhance small-target feature extraction. A bidirectional feature pyramid network (BiFPN) weights multiscale feature fusion to improve edge positioning accuracy of dense grains. Attention mechanism for fine-grained classification (AFGC) is embedded to focus on texture and damage details, enhancing adaptability to light fluctuations. The Detect_Rice lightweight head compresses parameters via group normalization and dynamic convolution sharing, optimizing small-target response. The improved model achieved 96.8% precision and 96.2% mAP. Combined with a quantity–mass model, SR/BR detection errors reduced to 1.11% and 1.24%, meeting national standard (GB/T 29898-2013) requirements, providing an effective real-time solution for intelligent hulling sorting.

As the four-way shuttle vehicle is prone to overturning in finished grain storage during acceleration and deceleration, an overturning mechanical model is constructed. The design factors analyzed include grain bag height, vehicle and grain bag mass, and the type of grain’s effect on the vehicle’s anti-overturning stability. The critical overturning condition of the shuttle vehicle during operation is also determined as a result of this analysis. With the critical overturning condition, the speed control of the shuttle vehicle based on the fuzzy PID method is carried out to improve the anti-overturning stability. Significant details are made about the fuzzification of input and output variables, determination of membership function, establishment of the fuzzy rule base, fuzzy reasoning, and defuzzification. Finally, the comparative simulation model of fuzzy PID and conventional PID in the MATLAB/Simulink platform showed that the fuzzy PID control method had the advantages of fast convergence and quick response to the speed control of the four-way shuttle vehicle.

The key research question in this study is how to cut pieces in irregular plate remainders, because there are many irregular plate leftovers created during the CNC (Computer Numerical Control) process. This will increase material utilization and allow plate leftovers to be reused. One of the issues being researched is how to arrange plate remainders on the surface of the CNC machine; this issue is known as combination layout optimization. The other issue being researched is combination cutting-path optimization of plate remainders, which aims to determine the cutting path of parts of plate remainders. A genetic algorithm based on the gravity-center NFP (No-Fit Polygon) method was applied to optimize the layout pattern, and then the geometric coordinates of a part included in one plate remainder after packing were obtained by geometric transformation with the help of a three-layer graphic data correlation model, which quickly identified the inside and outside contours of parts. A colony algorithm based on the mathematical model of cutting-path optimization was used to optimize the cutting path of the parts in the plate remainders. Finally, some simulation tests were performed to illustrate the feasibility and effectiveness of the proposed method. The results of the algorithm for packing irregular shapes for some instances show that our algorithm outperforms the other algorithms. On most instances, the average plate utilization ratio using our algorithm, after running 20 times, is improved by 1% to 9% in comparison to the best plate utilization ratio using the tree search algorithm. The best idle travel of an example achieved by the algorithm in this paper is 7632 mm after running the cutting-path optimization algorithm 20 times, while that of the traditional equivalent TSP (Traveling Salesman Problem) algorithm is 11,625 mm, which significantly demonstrates the efficiency of the algorithm.