In the current distributed manufacturing environment, more extensive enterprise cooperation is an effective means for shipbuilding companies to increase the competitiveness. However, considering the project scale and the uneven production capacity between the collaborative enterprises, a key issue for shipbuilding companies is to effectively combine the product-oriented project tasks and the specialized production-oriented plants. Due to information privatization, the decision-making process of project planning and scheduling is distributed and symmetric. Existing project scheduling methods and collaboration mechanisms in the shipbuilding industry are somehow inefficient. The aim of the research is to provide an assistant decision-making method to support effective task dispatching and multi-party cooperation for better utilization of the distributed resources and to help project managers control the shipbuilding process. The article initially establishes an agent-based complex shipbuilding project collaborative planning and symmetric scheduling framework, simulating the distributed collaborative decision-making process and bridging the multi-project planning with the individual project scheduling in much detail, which fills the research gap. A negotiation method based on iterative combination auction (ICA) is further proposed to solve the integration problem of project planning and task scheduling, and an illustrative example is conducted to demonstrate the effectiveness and rationality of the methods. Finally, an application case using a prototype system on shipbuilding projects collaborative planning and scheduling will be reported as a result.

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.

Hyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable for solving engineering optimization problems with complex constraints. This paper introduces a self-learning hyper-heuristic algorithm based on a genetic algorithm (GA-SLHH) designed to tackle the logistics scheduling problem of prefabricated modular cabin units (PMCUs) in cruise ships. This problem can be regarded as a multi-objective fuzzy logistics collaborative scheduling problem. Hyper-heuristic algorithms effectively avoid the extensive evaluation and repair of infeasible solutions during the iterative process, which is a common issue in meta-heuristic algorithms. The GA-SLHH employs a genetic algorithm combined with a self-learning strategy as its high-level strategy (HLS), optimizing low-level heuristics (LLHs) while uncovering potential relationships between adjacent decision-making stages. LLHs utilize classic scheduling rules as solution support. Multiple sets of numerical experiments demonstrate that the GA-SLHH exhibits a stronger comprehensive optimization ability and stability when solving this problem. Finally, the validity of the GA-SLHH in addressing real-world decision-making issues in cruise ship manufacturing companies is validated through practical enterprise cases. The results of a practical enterprise case show that the scheme solved using the proposed GA-SLHH can reduce the transportation time by up to 37%.

Aimed at enhancing the effectiveness and efficiency for offshore project scheduling, the mathematical model of two-stage scheduling is proposed according to the offshore project characteristics discussed in this article, which is used to solve the scheduling problem of large-scale and complex project. Regarding the two-stage scheduling, a system framework with multi-agent technology is presented to facilitate the integration and cooperation of offshore project scheduling. Next, the functional definition and internal structure of each intelligent agent is designed. Moreover, two kinds of intelligent algorithms have been designed to solve the resource conflicts and task assignment, respectively. Finally, the prototype system is developed to test the feasibility and rationality.

To solve the large-scale scheduling problem more efficiently within the requirements of the contract in shipyard, a three-layer parallel computing system was proposed. An optimized model for shipbuilding project scheduling problem was constructed under the condition of taking time and resource constraints into account. Moreover, the key techniques of proposed system were elaborated and the main steps were designed. In the first computing layer, the problem was decomposed into small parts in heterogeneous systems, reducing the problem scale; then, in the second layer, a co-evolution strategy for multi-populations was put forward to improve the algorithm robustness; in the third layer, a massive parallel computing method was performed under the Graphic Processing Unit structure. Finally, through two simulation examples, the robustness and outperforming others of the improved algorithm were verified.

The timeliness of the steel distribution center process contributes to the smooth progress of ship construction. However, carbon emissions from vehicles in the distribution process are a major source of pollution. Reasonable vehicle routing and scheduling can effectively reduce the carbon emissions of vehicles and ensure the timeliness of distribution. To solve this problem, a green vehicle routing and scheduling problem model with soft time windows was proposed in this study. An intelligent water drop algorithm was designed and improved and then compared with the genetic algorithm and the traditional intelligent water drop algorithm. The applicability of the improved intelligent water drop algorithm was demonstrated. Finally, this algorithm was applied to a specific example to demonstrate that the improved intelligent water drop algorithm effectively reduced the cost of such green vehicle problems, thus reducing the carbon emissions of vehicles during the distribution process and achieving reductions in environmental pollution. Ultimately, this algorithm facilitates the achievement of green shipbuilding.