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.

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%.

In the construction process of large cruise ships, there are numerous cabin components, and the number of assembly sequences will experience a “combinatorial explosion”, which will become a complex NP hard problem. This article proposes an assembly sequence planning method based on practical engineering problems in the construction process of large cruise ships. The cabin components are modularized, and an optimization algorithm is designed for multi-objective problem solving to obtain the optimal assembly sequence of cabin components. This article analyzes the impact of six constraint conditions on the assembly plan, including geometric constraints, sequence constraints, number of assembly reversals, number of tool replacements, stable connection relationships, and selection of reference components. A fitness function is designed and a mathematical model is established. On this basis, a genetic greedy combination algorithm is proposed to solve the optimal assembly sequence. Compared with traditional genetic algorithms, this improves computational efficiency and solves complex problems in a better manner. Multiple unique optimal solutions can be obtained in one solution process. The feasibility and effectiveness of this method were verified through examples.

This study examines the challenges related to the storage and distribution of cabin materials on board during the construction of cruise ships. The construction process of cruise ships involves the assembly of various cabin materials, which are temporarily stored and transported in a complex deck environment with multiple obstacles. These processes can be viewed as location-routing problems (LRPs). However, existing solutions for LRPs do not account for the obstacles commonly encountered in deck environments. Therefore, this paper introduces a new variant of LRPs, termed as the BE-LRP (blocking environment location-routing problem). Initially, by considering the optimization objectives of minimizing the distribution distance and the number of vehicles required, the paper develops an optimization model for the location-routing of on-board materials while taking obstacles into consideration. Subsequently, a method for estimating blocked distances is proposed. This method utilizes Gaussian process regression to predict blocked distances and enables the rapid estimation of distances obstructed by obstacles between different destinations. Lastly, a hybrid obstacle blocking distance processing HO-NSGAII algorithm is formulated to address the BE-LRP. Experimental comparisons demonstrate that the distribution scheme derived from this research method does not necessitate modifications to the delivery distance when compared to the traditional NSGAII algorithm, and better aligns with the actual deck conditions. With an equivalent number of delivery vehicles, the scheme results in shorter delivery distances. The delivery distance can be reduced by 3–17% under varying circumstances. These findings are advantageous for enhancing the efficiency of cruise ship construction and mitigating the impact of warehousing and distribution on construction progress.

The utilization of PMCUs (Prefabricated Modular Cabin Units) is a key strategy for enhancing the efficiency and reducing the costs of large cruise ship construction. Effective transportation planning for multiple PMCU cabins, including the paths and sequence, is vital to ensure smooth and timely installation. However, traditional A-Star algorithms for path planning face limitations when it comes to accounting for boundary obstacles, and existing routine sequence planning methods can hardly tackle multi-cabin coupled blocking effects. To address these challenges, an enhanced A-Star algorithm is introduced and designed to handle both grid-based and boundary obstacles in the path planning process for cabin transportation onboard. Additionally, a novel reverse planning strategy is further proposed that addresses the coupling effects arising from inter-cabin mutual blocking by determining the optimal collision-free installation sequence. Integrated with the enhanced A-Star algorithm, this reverse planning strategy effectively eliminates interference between cabin transportation sequences and transportation paths, significantly improving operational efficiency. A comparative analysis across various scenarios further substantiates the practicality and effectiveness of the proposed method, highlighting its potential for real-world application in large-scale cruise ship construction.

Modular shipbuilding, as a cutting-edge ship construction paradigm, enables parallel manufacturing across workshops and stages—a core advantage that significantly shortens the total shipbuilding cycle, making it pivotal for modern shipyards to enhance productivity. However, this mode decomposes the integrated shipbuilding project into a large number of interdependent sub-activities spanning three key stages (fabrication, logistics, and assembly). Further, the duration of these sub-activities is inherently uncertain, primarily due to the extensive manual operations, variable on-site conditions, and supply chain fluctuations inherent in shipbuilding. These characteristics collectively pose a formidable challenge to project planning that pursues both high efficiency and low cost. To address this challenge, this paper proposes a Strategy-Group Evolution algorithm. First, the modular shipbuilding process scheduling problem is mathematically formulated as a resource-constrained three-stage multi-objective optimization model, where triangular fuzzy numbers are employed to characterize the uncertain sub-activity durations. Second, a two-layered Strategy-Group Evolution algorithm is designed for solving this model: the inner layer comprises 12 practical priority rules tailored to modular shipbuilding’s multi-stage features, while the outer layer adopts a genetic algorithm-based evolution policy to schedule and optimize the assignment of inner-layer rules to activity groups. The core of the Strategy-Group Evolution algorithm lies in dynamically assigning suitable strategies to different activity groups and evolving these assignments toward optimality—this avoids the limitation of a single priority rule for all stages, thereby facilitating the search for global optimal solutions. Finally, validation tests on real cruise ship construction projects and benchmark datasets demonstrate the efficacy and superiority of the proposed Strategy-Group Evolution algorithm.