Automatic Design for Pipe Arrangement using Multi-objective Genetic Algorithms

Abstract

This paper presents an automatic design method for pipe arrangement. A pipe arrangement design problem is proposed for a space in which many pipes and objects co-exist. This problem includes large-scale numerical optimization and combinatorial optimization problems, as well as two criteria. For these reasons, it is difficult to optimize the problem using usual optimization techniques such as Random Search. Therefore, a multi-objective genetic algorithm (GAs) suitable for this problem is developed.A pipe is characterized by both a pattern of generation and numerical parameters. The former describes the way the pipe bends and the latter details the length of the straight parts. For this reason, a combination of the pattern of generation and the numerical parameters is used for the solution representation and a new method of crossover is proposed that takes into account interference with obstacles. As the number of pipes increases, it becomes rapidly more difficult to find feasible solutions where pipes do not interfere with each other. Therefore, two modification operators that transform infeasible solution candidates into feasible ones are introduced. One operator modifies the pipe having a lot of interferences with other pipes so that it will not interfere with them, and the other is related with the operation that modifies the pipe that travels through obstacles. Although there are cases in which pipes cannot completely avoid obstacles in practical designs, this situation is taken into consideration by this design process.The proposed method for optimizing a pipe arrangement efficiently is demonstrated through several experiments, and remarks are provided for applying this methodology to a practical pipe arrangement design.

An Automatic Piping Algorithm including Elbows and Bends

“…The minimum vertical length of the bend is computed by (2) where β is the value calculated through equation (1). In our experiments, the value of α1 and α2 are set at 5, 0, respectively, and the value of d is limited in the diameter of the pipe.…”

“…In case of searching the next node through the bend, the algorithm searches nodes which exist in the place away from the length L calculated through equation (2). From these nodes, the algorithm chooses the closest node as the next node.…”

“…Once the temporary node will be found, the algorithm will search the next node in Figure 4. The next node will places farther than the L calculated through equation (2) from the temporary node.…”

“…Many previous works about an automatic piping system have been done in many approaches to solve the piping problem [1], [2], [3]. And some of these works, such as Ito [4], Park et al [5], Asmara et al [6], [7], and Paulo et al [8], applied for Cell Decomposition approach.…”

The development of a practical pipe auto-routing system in a shipbuilding CAD environment using network optimization

“…Examples include an evolution-based algorithm such as a genetic algorithm (Ito, 1999;Ikehira et al, 2005;Kimura, 2011) and an ant colony optimization scheme (Xiaoning et al, 2006(Xiaoning et al, , 2007. The target of route optimization is usually the minimum cost of the pipe routing path.…”