Auto-routing Methods for Complex Ship Pipe Route Design

Abstract

Pipe route design is one of the most complex and time-consuming steps in the ship detailed design. A large number of scholars have studied the optimization of ship pipe routes, but there is still a lack of a fast and effective method for the automatic design of complex pipe routes. For this reason, this article proposes the auto-routing methods of single pipes, branch pipes, and multipipes. The auto-routing methods are based on an Improved A* algorithm. Compared with the traditional A* algorithm, the Improved A* algorithm considers the constraints of ship pipe route design. And by introducing “Blocking Iteration” and dynamic weight, the accuracy and efficiency of the algorithm are improved. On this basis, this article uses Python and OpenGL to develop the automatic pipe route design system. The system supports the automatic pipe route design, visualization of optimization results, and two-way interaction with three-dimensional (3-D) design software. Finally, the practicability of the proposed methods and system is verified by the simulation on a comparison case and an actual fuel filling and transfer pipe system.

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

“…However, its effectiveness is compromised as it fails to consider the factors influencing piping routes. The design of piping routes is a complex and time-consuming process within the design field [92].…”

The Intelligent Layout of the Ship Piping System Based on the Optimization Algorithm

“…To verify the optimized A*-genetic algorithm proposed in this paper in the actual pipeline intelligent arrangement, the optimized A*-genetic algorithm, the A* algorithm of Bian [18], the particle swarm algorithm of Dong [21], and the maze-genetic algorithm of Sui [26] are used to search for the optimal solution of the pipeline in the set pipeline arrangement space. The specific basic parameter settings of each algorithm are shown in Table 3.…”

“…Compared with it, the optimized A*-GA proposed in this paper completes the crossover and mutation process through the A* algorithm with higher search efficiency, which further improves the search efficiency of the traditional genetic algorithm. The optimized A*-genetic algorithm proposed in this paper, the A* algorithm of Bian [18], the particle swarm algorithm of Dong [21], and the maze-genetic algorithm of Haiteng Sui [26] are each repeatedly run 15 times in the set pipeline arrangement space for the intelligent arrangement of pipelines. The specific results are shown in Table 5.…”

“…Compared with MA-GA, PSO, and A*, the optimized A*-genetic algorithm proposed in this paper can effectively improve the piping arrangement efficiency while ensuring the piping arrangement quality, which verifies the adaptability and superiority of the algorithm for the intelligent arrangement of single piping of ships. Simulation experiments are performed for Case 3 using the optimized A*-genetic algorithm with the introduction of auxiliary connection points and the A* algorithm of Bian [18], and the specific parameter settings are shown in Table 6. It should be noted that the algorithm parameters that need to be set in the parallel pipeline have more auxiliary connection points than the single pipeline.…”

“…Asmara [17] studied the problem of ship pipeline arrangement using an optimized Dijkstra algorithm, but the effect of this method is more general for branch pipelines with complex structures. Bian et al [18] improved the traditional A* algorithm by introducing the "block iteration" method and dynamic weights to make it applicable to the ship piping arrangement problem and enhance the solution efficiency. However, the likelihood of finding the optimal piping arrangement could be higher.…”