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Int. J. Naval Archit. Ocean Eng. (2013) 5:468~477

http://dx.doi.org/10.3744/JNAOE.2013.5.3.468

ⓒSNAK

,

2013

The development of a practical pipe auto-routing system in

a shipbuilding CAD environment using network optimization

Shin-Hyung Kim1, Won-Sun Ruy2 and Beom Seon Jang3

1R&D Institute, Daewoo Shipbuilding & Marine Engineering Co., Korea

2Department of Naval Architecture and Ocean Engineering, Chungnam National University, Daejeon, Korea

3Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul, Korea

ABSTRACT: An automatic pipe routing system is proposed and implemented. Generally, the pipe routing design as a

part of the shipbuilding process requires a considerable number of man hours due to the complexity which comes from

physical and operational constraints and the crucial influence on outfitting construction productivity. Therefore, the

automation of pipe routing design operations and processes has always been one of the most important goals for im-

provements in shipbuilding design. The proposed system is applied to a pipe routing design in the engine room space of

a commercial ship. The effectiveness of this system is verified as a reasonable form of support for pipe routing design

jobs. The automatic routing result of this system can serve as a good basis model in the initial stages of pipe routing

design, allowing the designer to reduce their design lead time significantly. As a result, the design productivity overall

can be improved with this automatic pipe routing system.

KEY WORDS: Pipe routing; Automatic routing; Shipbuilding customized Computer Aided Design (CAD)-based system.

INTRODUCTION

The pipe routing design as it pertains to shipbuilding is usually performed during the basic and detail design stage after the

creation of the pipe & instrument diagram (P&ID), which contains connection data between equipment in the preliminary de-

sign stage. Generally, this type of pipe routing design is accomplished by a highly experienced designer who can consider not

only the complex shapes and connections of each piece of equipment but also the issues of space availability, material costs,

accessibility, and suitability for installation. The amount of pipe routing work is nearly a half of all outfitting design work at that

stage. In addition, the quality of the pipe routing design effort has a direct effect on the subsequent construction design stage, on

which the total material and construction cost strongly depends (Shao et al., 2009), just like other design work in the detail des-

ign stage of shipbuilding.

When we consider the Just-In-Time (JIT) production scheme in the area of shipbuilding, not only the quality of the routing

design which guarantees an accurate amount of raw materials for pipe construction but also the on-time delivery of the routing

result to the subsequent design stage are very important in JIT production (Koenig et al., 2002). Moreover, every ship has diffe-

rent specifications, except for a few sister ships. Therefore, every ship needs to be designed based on individual specifications.

Consequently, the ratio of the design cost to the total building cost is significantly higher in the shipbuilding industry. Therefore,

pipe-routing design automation schemes with feasible quality results that are delivered on time have been key issues to those

seeking shipbuilding design process improvements.

Corresponding author: Won-Sun Ruy

e-mail: wsruy@cnu.ac.kr

Int. J. Naval Archit. Ocean Eng. (2013) 5:468~477 469

PIPE AUTOMATIC ROUTING

Routing optimization problem

The pipe routing optimization problem should satisfy the various constraints. Park (2002) and Qian et al. (2008) catego-

rized these constraints into the two groups of restrictive constraints and quantifiable constraints. Some of them are discussed

below.

•

Physical Constraints

The pipe routing should avoid physical obstacles and connect to the proper equipment.

•

Economic Constraints

The pipe routing should minimize the total material and fabrication cost by reducing pipe lengths and number of bent parts

and by increasing shared pipe supports.

•

Operational Constraints

The pipe routing should consider the proper operations like valve accessibility and clearance from some equipment for safety.

Physical and operational constraints are restrictive while economic constraints are quantifiable. Therefore, pipe routing opti-

mization seeks to find the best path from an economic point of view among the set of feasible paths restricted by physical and

operation constraints.

Related works

Various types of optimization algorithms have been applied to the pipe routing problem. In an early example, the Maze

algorithm was proposed by Lee (1961). This algorithm divides a space into cells and labels and chooses the next cell until the

target cell is reached. Hightower (1969) proposed the escape algorithm, also known as the line-search algorithm. This is shown

in Fig. 1.

Fig. 1 The escape algorithm proposed by Hightower.

Some network-based algorithms can be used to solve various problems (Nicholson, 1966; Ando and Kimura, 2011). In

network-based optimization, each vertex vi denotes the junction of a pipe where a bent pipe part can be placed; the edge eij

between the vertexes vi and vj denotes a straight pipe part with cost cij. Fig. 2 shows a graph representation of this.

Fig. 2 A graphic representation of pipe routing between equipment.

470 Int. J. Naval Archit. Ocean Eng. (2013) 5:468~477

G = (V, E, C) (1)

In Eq. (1), V denotes the set of vertices, E is the set of edges, and C denotes the cost. The pipe routing optimization

problem is to find the shortest path between the start vertex s and the end vertex f in the graph G in Fig. 2. These traditional

routing algorithms with graph representation are generally based on what is known as the ‘cell decomposition’ approach. The

cell decomposes the problem space containing the start and end points of the target equipment into cubic cells to reduce the

problem size and represent the pipe path through the sequencing of connected cells. A good example was given by Asmara

and Nienhuis (2006). Ito (1999), Park (2002) and Ando and Kimura (2011) also applied for this approach to represent the pipe

routing path.

To find the global optimum route path, several efforts have been made. 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, 2007). The target of route optimization is usually the minimum cost of the pipe routing path. In many studies, the cost

consists of the pipe length cost and the cost of all bent parts, which require expensive bending fabrication or elbow fitting

processes. Park (2002), Kimura and Ikehira (2009) and Ando and Kimura (2011) also considered the operability costs such as

the costs incurred to determine valve locations and safety clearances.

Much research has been done since the 1970s. However, there are still limitations when attempting to make use of it to

create a fully automatic routing system for actual shipbuilding design work. As discussed by Missuta et al. (1986) and Kang et

al. (1999), the main reason for this is that pipe routing algorithms generally do not consider the knowledge and the preference of

the designer suitably as required in the actual design work. This type of limitation is not a matter purely related to the optimiza-

tion algorithm itself. It is rather a matter of knowledge representation during the design automation process (Sriram et al., 1989).

Therefore, the knowledge representation in the design has become a more important issue in the area of design automation.

Moreover, from a practical point of view, it is also important that the implemented routing algorithm can be utilized effectively

in an actual shipbuilding design environment. Some pipe routing algorithms are evaluated in the form of a design support

package program; these have a neutral data interface to a CAD system for practical use (Sandurkar and Chen, 1999; Asmara

and Nienhuis, 2006; Paulo and Lobo, 2009). They use text-based neutral files such as standard tessellation language (STL) for

interfacing data to the CAD system. After constructing the pipe network, Ruy et al. (2012) studied a hole plan system recently.

Network based routing algorithm

The pipe routing algorithm developed in this research is based on a network optimization algorithm. The target space inclu-

ding target equipment is divided into non-uniform cells. The graph is constructed considering the route constraints of the fitness

of the space, pipe length and bending. This graph represents the pipe route in the target space. An optimum route path can then

be obtained by a general minimum path-finding algorithm.

Cell decomposition

Cell decomposition is useful strategy for reducing the problem size. Cell decomposition divides a continuous target space on

which pipe lines and equipment are positioned into discrete cells. The edges and vertexes of the divided cubic cells can represent

the edges and vertexes of the graph, which connect the start and end points of the routing path. In this case, the network based

routing algorithm can be practically applied to these graphs in a reduced problem space. One of the major issues associated with

this cell-division method is the number of cells. A larger number of cells generally guarantees a better route path but takes more

time to calculate. Therefore, the number of cubic cells should be controlled considering the characteristics of the problems.

A pipe path along the wall or ceiling structures is preferred due to the issue of space availability after the routing process. This

indicates that this near wall space has higher fitness than other spaces. In this research, the target space is divided non-uniformly

according to the degree of the special fitness. A space with higher fitness is divided into smaller cubic spaces. The vertexes and

edges of cubic cells can be used as the vertexes and edges of the graph which contains a candidate route path. Thus, the space with

higher fitness has denser cubic cells and more candidate routes. In contrast, the space around passageways or equipment which

may require some distance is divided into larger cubic cell. This cell-division strategy is a part of the consideration of routing de-

sign practices pertaining to space fitness. Fig. 3 shows non-uniformly divided cells in the target space. Each cubic cell can have its

own fitness factor, and this fitness factor has an effect on the cost decision (value Cij) between the vertices in Fig. 2.

Int. J. Naval Archit. Ocean Eng. (2013) 5:468~477 471

Graph construction

As mentioned above, the vertices and edges of a cubic cell in the target space could be the vertices and edges of the graph.

Though the numbers of vertices and edges are reduced by cell decomposition, it is still possible for the graph to be more sim-

plified while maintaining a feasible route path in the target space. To construct a simpler graph, a vertex construction strategy is

developed. It is essentially based on the escape algorithm proposed by Hightower (1969). The original escape algorithm is fast

and simple, producing one solution directly, as shown in Fig. 3, but it cannot guarantee a solution (Kai-jian and Hong-e, 1987).

The vertex construction method of this system uses a strategy similar to that of the escape algorithm to expand the route graph.

An edge runs like a beam until it encounters the side of an obstacle or a wall boundary of the target space, at this point it

branches off in another direction, as shown below in Fig. 4.

Fig. 3 Non-uniformly divided cells. Fig. 4 A graph construction by the vertex branching.

A branch also could be made during edge runs when it meets the edges of cubic cells in the target space. Therefore, a dense

area with smaller cubic cells in the target space has more candidate vertices; i.e., it is a preferable space with more candidate

vertices.

The vertices are located on the corners of non-uniformly divided cubic cells and are connected by edges which have their

own weight factor, as shown in Fig. 5. Basically, a network graph is characterized only by the vertices and weighted edges be-

tween them. In terms of this traditional definition of a graph, the two graphs in Fig. 6 are equivalent. The relative location of the

vertices makes no difference while the connection is maintained.

Fig. 5 Vertex located on the corner of a cubic cell.

472 Int. J. Naval Archit. Ocean Eng. (2013) 5:468~477

However, the vertex and edge connection of the graph applied in this system needs to have a topological meaning, as the

graph should represent the physical pipe routing. An edge connection running in the target space corresponds to the pipe

routing and the vertex on the corner of a cubic cell also contains location information. In this new definition, the equivalent

graphs in Fig. 6 are not the same. The figure on the left is a straight pipe and the right side is a bent one. This bending route

needs the pipe-bending or an additional elbow fitting pipe part, which generally increases the cost compared to a straight

pipe. Therefore, this type of bent pipe should be considered as a cost factor in the route optimize algorithm. To consider a

bent pipe such as this one in the graph, a vertex split strategy is introduced. This strategy simply removes the ambiguousness

of vertex connections.

Fig. 6 Equivalent graphs.

The left figure in Fig. 7 is a part of a route graph with a directional edge, showing each weight factor. In this graph, route A-

B-D is a straight route while route C-B-D is a bent one. However, the edge between B and D also has a single weight factor of 5;

it can be part of A-B-D and part of C-B-D at the same time. Therefore, a pipe bent in this manner is not suitable for this type of

graph structure. The right graph of Fig. 7 illustrates the vertex split strategy. Vertex B is split into B and B’ with a bending pe-

nalty weight factor of 6. The graph is reconstructed with route A-B-D and route C-B’-D. Then, the bending of the route could

be considered without ambiguity.

A vertex with two or more incoming edges and outgoing edges should be split when outgoing edges can be used in a diffe-

rent route path, straight or bent simultaneously. In the case shown in Fig. 8, there are two split vertices. Practically, in a 3D cu-

bic cell space where the number of neighbor vertices cannot exceed six, the number of split vertices is at most three in a case

with three incoming edges and three outgoing edges.

Fig. 7 Vertex split 1. Fig. 8 Vertex split 2.

Fig. 8 shows another example of a vertex split. This vertex split strategy expands the route graph for mapping onto an actual

pipe route in the target space considering the relative locations of the neighbor vertices. The weight factor of an edge is pro-

portional to the penalty factor expressed by Eq. (2), which is evaluated according to the distance and space factor of the edge

location and the bent condition.

Penalty factor = distance * distance factor + bending factor + space factor (2)

Eq. (2) accounts for the component of the weight factor, the distance is the actual distance between two vertices, and the

distance factor adjusts for the effect of the distance in the weight factor. The bending factor has a positive value when an edge is

a part of a bent route. On the other hand, the space factor shows the fitness of the space; here, a smaller value means better

fitness. For example, a cubic cell located near equipment, a wall or a ceiling has a smaller space factor because it is a preferable

space.

Int. J. Naval Archit. Ocean Eng. (2013) 5:468~477 473

Routes A-B and C-D-E-F in Fig. 9 have the same distance. If the space factor (a route along wall is recommended generally

recommended) is more important than the bending factor, route C-D-E-F would be chosen; otherwise, route A-B with less ben-

ding would be selected by the optimization algorithm.

Fig. 9 Bending route along a wall side.

Shortest path-finding algorithm

After the vertex distribution and weight decision of the edges, the path-finding algorithm to find the shortest path for the

graph can be applied. The shortest path of the graph may be the best pipe route because the weights of the edges represent the

total cost of the route, including the materials, fabrication cost, operation cost, and other factors. The graph constructed with

these rules is a directional and positive single graph. Therefore, Dijkstra’s algorithm can be applied. For the simplest implemen-

tation, it is known that the running time of Dijkstra’s algorithm is in O(V2). Therefore, the construction of the simpler graph G is

an important process in the development of this automatic routing scheme. The non-uniform cell decomposition and the vertex

location strategy can reduce the size of the graph considerably as well.

Design practice management

The sizes of the non-uniformly divided cubic cells and the edge weight factors are all based on the practices of the pipe

routing designer. This practice should be represented and controlled explicitly in the system to be used by a designer who may

not have enough pipe routing experience or who wants to accomplish the pipe routing tasks quickly.

In this development, the design practice is basically represented as a parameter. Some aspects, such as the wall side pre-

ferences, can be represented by a parameter set that affect the fitness of the space near the side wall, while other practices such

as grouping pipes on the same pipe support cannot easily be represented by a parameter set. Therefore, the former is represented

in the form of a parameter set and the latter remains as work needing to be done by the designer, who can review and modify

the automatic routing result.

The parameters are classified into two groups, one for cell decomposition and the evaluation of the space fitness, and the

other for the construction of an efficient graph. The designer can choose a pre-defined parameter set or modify each parameter

depending on their preferences. It should be noted that this approach cannot cover all tasks needed for the full automation of the

pipe routing routine. Further studies are necessary to accomplish this. Note that a rapid design cycle including automatic routing

and route modification, however, can still be practically accomplished.

IMPLEMENTATION

System configuration

The automatic routing system consists of three modules. The first module is for input data creation, the second one is for

routing optimization, and the third one is for the resulting pipe model and for its modification. The input data module and pipe

model creation module are embedded in the CAD system Tribon M3 of AVEVA Marine Design and Engineering, while the

routing optimization module is a standalone program.

This CAD system provides an application program interface (API) written in Python script. Therefore, the main structure of

this automatic routing system is written in Python and the mathematic library and GUI are written in C++. These two embedded

474 Int. J. Naval Archit. Ocean Eng. (2013) 5:468~477

modules exchange data with the CAD system directly via the API and the standalone optimization module receives and sends

data via the XML file format. Fig. 10 presents the configuration of the automatic routing system.

Fig. 10 Configuration of the automatic pipe routing system.

Input data creation module

The input data creation module obtains the equipment connection data from the P&ID and the equipment property data

from the CAD database. It also has a user input interface with which the designer can input or modify the space and equipment

data. This module generates cell decomposition data and equipment data and sends this data to the routing optimization module.

The equipment volume is simplified as a cuboid and the locations of the pipe connections on the equipment are modified de-

pending on the direction of the flow for practical reasons. The target space is divided non-uniformly according to the space

fitness and the equipment volume is then subtracted. Fig. 11 shows the input data creation module integrated in the CAD

system. The designer can select and change the parameter set as they wish, and this selected parameter set governs the routing

result.

Fig. 11 Integrated input data module.

Int. J. Naval Archit. Ocean Eng. (2013) 5:468~477 475

Route optimization module

The route optimization module divides the target space non-uniformly and constructs a network graph with the vertex and

edges of the cubic cell. Fig. 12 shows an example of a constructed route path graph with start point A and end point B.

Fig. 12 A example of a constructed graph.

Pipe model creation module

This module creates an actual pipe model in the CAD system with the routing result from the optimization module and the

pipe specification data from the input data module. This module runs on the pipe modeling CAD system. Thus, the designer can

modify or confirm the pipe model in the same user environment.

This module also generates the pipe model data for the following stage of production, including the design and preparation

stages. One of the most important types of model data is the bill of materials (BOM), which contains information about the raw

materials and the construction processes. The BOM of this system would be accurate and on time because it is based on the

accurately and quickly created pipe model. This feature of the BOM plays an important role in the JIT production processes.

Route optimization result

This automatic routing system is applied to pipe routing in a ship engine room space. The volume of the engine and some

equipment in the target space are shown in Fig. 13.

Fig. 13 Engine room CAD model.

476 Int. J. Naval Archit. Ocean Eng. (2013) 5:468~477

Fig. 14 A graph construction in an engine room and the result of automatic routing from A to B.

The left side in Fig. 14 shows a simplified graph of the equipment volume and candidate pipe routing. The graph is cons-

tructed according to design as represented by the parameters. The right side in Fig. 14 shows the routing result from the start A

to end B points.

This system can produce a practical pipe-routing result, showing the amount of pipe material and the approximate route

path information. This type of information is useful during the basic design stage. However, this auto-routing pipe model

should be checked whether detailed pipe routing practices and rules are considered. These considerations include the air poc-

kets, the thermal expansion, the alignment of support structures, and the valve accessibility, for instance, at the detail design

stage. Moreover, some path modification is necessary to utilize the extra space caused by the cuboid simplification of the

equipment volume and to reduce the degree of complexity in the narrow spaces through the use of diagonal piping and bent

pipes instead of elbow fittings.

In an actual pipe routing job in a shipbuilding outfitting design, there are numerous pipe lines in a single work area (e.g., a

block, a room); moreover, there are not only simple pipes which have single start and end point but also pipes with bypasses or

branches. This system can handle multiple pipes via the input data creation module, and the automatic pipe routing is done

sequentially. Therefore, the sequence set by the designer in the input data creation module characterizes the total routing result

of multiple pipes. And this system can handle only simple pipes, the pipe lines with bypass or branch should be divided into

simple one in the input data creation module.

CONCLUSIONS

A practical automatic pipe routing system is developed and integrated in a shipbuilding CAD system. The pipe routing algo-

rithm is based on graph optimization. A graph containing candidate route paths is constructed on a target space composed of

ununiformly divided cubic cells. The design preferences are represented and managed by parameters.

This system is applied to the engine room area of a commercial ship. The result shows that the system cannot always pro-

duce the best pipe route initially. However, the designer can recalculate the result easily and quickly with a new parameter set to

obtain a satisfying result in the integrated CAD environment. However, there are several limitations of this system. These are

mainly related to the management of design knowledge and practices which are rarely expressed by a simple parameter. How-

ever, the routing result can serve as a good basis for data generation tasks such as the creation of a BOM and for detailed pipe

routing with slight modifications. As a result, the lead time of the basic pipe design stage can be reduced and more accurate and

earlier product model data can be produced. These features of the system can improve the productivity of outfitting design and

construction in the shipbuilding industry.

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