In the era of Industry 4.0 to 5.0, the manufacturing industry is dedicated to improving its production efficiency, control capability and competitiveness with intelligent enhancement. As a typical discrete manufacturing industry, it is difficult for ocean engineering (OE) manufacturers to accurately control the entire production process, and the establishment of an integrated system supported by digital twin (DT) technology is a better solution. This paper proposes a comprehensive set of system architectures for the DT workshop. It focuses on planning, which is the main line of control, to establish a model based on graph neural networks (GNNs) and suggests five decision-support approaches associated with the model from a practical application perspective. The utilization of complete twin data for prediction and visual simulation effectively eliminates the problem of unexpected factors interfering with scheduling in enterprise production planning and achieves the goals of rapid processing and just-in-time completion. The planning model is based on the attention mechanism, which characterizes the disjunctive graph, extracts the input GNN, and outputs the scheduling decision by constructing the multi-attention network of operations and machines to deal with the complicated “operation–machine” combination relationship. The proposed method has been verified in the case of structural assembly and welding workshops, has validity and reliability, and is superior to the traditional priority scheduling rules and heuristics in terms of precision rate and rapidity. Furthermore, the DT system completes the production line application, and its proven reliability supports its full-scale application in future smart factories.

Quality traceability plays an essential role in assembling and welding offshore platform blocks. The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry. Currently, quality management remains in the era of primary information, and there is a lack of effective tracking and recording of welding quality data. When welding defects are encountered, it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data. In this paper, a composite welding quality traceability model for offshore platform block construction process is proposed, it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm. By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm, the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems. Furthermore, the model and the quality traceability algorithm are checked by cases in actual working conditions. Verification analyses suggest that the proposed early-warning model for welding quality and the algorithm for optimizing backtracking requests are effective and can be applied to the actual construction process.

In the context of increasingly competitive shipbuilding, the flexible multi-level picking system, composed of high-rise shelves, Automated Guided Vehicles (AGVs), and picking stations, has been of gradual interest because of its advantages in operation efficiency, system flexibility, and system robustness. Compared with other simple-level systems, the flexible multi-level picking system has a more complex coupling temporal relationship, which makes the scheduling optimization of shipbuilding automated collaborative order picking (SACOP) extremely difficult. In order to avoid the dilemma of finding a feasible and optimal collaborative scheduling scheme under the constraints of a complex temporal relationship, this paper proposed a multi-AGV-driven pallet-picking scheduling optimization (MADPSO) method, which takes the AGV scheduling scheme as the direct solution and modifies it to a feasible solution under the reasonably designed interaction strategy of stacker, AGV, and the interaction strategy of picking station, AGV. Furthermore, taking the minimum energy consumption and operation time as the optimization objectives, a multi-objective optimization mathematical model was established to describe MADPSO, and an improved NSGA-III algorithm was designed to solve the problem. Finally, several experiments were conducted in various scenarios and verified that using MADPSO can achieve a comprehensive optimization index improvement of 52.02–75.66% compared with traditional picking methods, which has a certain reference significance for shipyards.

In order to complete the offshore platform project scheduling intelligently, an improved scheduling optimization system based on the parallel genetic algorithm was proposed. An optimal model for the large‐scale offshore platform project scheduling problem (LSOPPSP) was built and produced the mathematic model of LSOPPSP, based on the characteristics of the abundance of activities, long duration, high uncertainty, and frequent changes. In long‐term unsteady manufacturing, this model can provide good robustness. In addition, the essential steps of the multitime window parallel genetic algorithm were proposed. An improved population initialization algorithm was designed, as well as the coevolution strategy among populations was also proposed in parallel computing. These two strategies can increase population variety while also speeding up convergence. Finally, the suggested parallel scheduling system was deployed in our self‐developed schedule optimization software for offshore platform enterprises, and the outperformance of the improved algorithm was proven by simulated examples and practical application.