To ensure unmanned vehicles can perform a sortie mission quickly, efficiently, safely and reliably after receiving the command, it is necessary to calculate the sortie mission reliability of the shipborne unmanned vehicle group before loading. Aimed at the layout and sortie characteristics of an unmanned vehicle group, a sortie mission network model and a calculation method for sortie mission reliability are designed in this paper. Firstly, this paper uses space partition to parallel search for equal-length minimal paths based on the two-terminal network reliability. Secondly, this paper adopts the sum of disjoint products to process the equal-length minimal path set, innovatively proposing a calculation method for the sortie mission reliability of the shipborne unmanned vehicle group. Finally, the sortie mission reliability for three typical cases was calculated and compared with the Monte Carlo method. The comparative analysis indicates that the proposed method is both accurate and efficient, thereby corroborating its scientific validity and practical effectiveness. This study fills the gap in the field of sortie mission reliability and lays a theoretical foundation for subsequent research. Meanwhile, the method proposed in this paper can also be extended to the reliability calculation of a multiple-vehicle sortie mission in similar enclosed spaces.

Conventional methods for calculating sortie mission reliability of shipborne vehicle layouts suffer from excessive computational overhead, long runtimes, and large labeled data requirements. To address these limitations, this work proposes a specialized graph neural network architecture tailored for limited-data small-sample scenarios, denoted as the Small-Sample Graph Neural Network (SS-GNN). The proposed SS-GNN integrates multi-relational graph convolutional layers, an adaptive attention weighting mechanism, small-sample regularization techniques, and an uncertainty quantification module to accurately capture the heterogeneous multidimensional dependencies between vehicles. To further improve learning performance under data-scarce conditions, we employ a hybrid training strategy combining meta-learning-based pretraining, contrastive learning for representation enhancement, knowledge distillation, and transfer learning. Experimental results demonstrate that SS-GNN substantially outperforms traditional reliability calculation methods, classical machine learning models, and state-of-the-art GNN baselines across three key dimensions: predictive accuracy, computational efficiency, and generalization robustness, while also providing theoretically grounded uncertainty estimates for all predictions. This work provides both a theoretical foundation and a practical technical framework for shipborne vehicle reliability prediction and offers a generalizable solution for small-sample graph regression tasks in industrial domains. Future work will focus on extending the approach to extremely low-data regimes via specialized few-shot learning algorithms, incorporating dynamic relation modeling for time-varying sortie processes, and integrating domain knowledge graphs to broaden its operational applicability.

The vehicles’ layout is critical for amphibious landing operations, maritime emergency rescue, and roll-on/roll-off vessel loading. Conventional layout algorithms in Cartesian coordinates focus primarily on maximizing space utilization, yet they overlook congestion that may arise from equipment failures during actual layout, leading to a sharp decline in mission reliability. To address this issue, this paper proposes the Lowest Contour Line Algorithm based on polar-coordinate. Unlike traditional heuristic methods, it explicitly incorporates reliability metrics into the layout process, thereby producing layout schemes with higher sortie mission reliability. By arranging vehicles’ radially and deriving the initial contour from turning radii, the algorithm raises the lower bound of mission reliability. In addition, a scan-point placement strategy, a contour update mechanism, and a circuit breaker mechanism are proposed to balance space utilization and mission reliability. Comparative experiments with three baseline methods demonstrate that the proposed algorithm significantly improves sortie mission reliability while maintaining a number of layout units comparable to that of conventional approaches. The algorithm provides a novel technical solution for layout scenarios with stringent reliability requirements.