11
Pipe-routing algorithm development: case study of a ship engine room design Jin-Hyung Park * , Richard L. Storch Industrial Engineering, University of Washington, Box 352650, Seattle, WA 98105, USA Abstract This study presents an automatic pipe-routing algorithm accommodating all major detail-design facets. First, the algorithm uses pattern- match methods to provide candidate paths. A cell-generation method is developed which satisfies geometric constraints. This makes the generation and evaluation of paths effective and efficient. Next, various non-geometric aspects, such as material costs, installation costs, and valve operability, are assessed from a fiscal point of view. Then, from a tree of combinations, the algorithm chooses an appropriate path for each pipeline from the candidate paths. Finally, a general approach toward detail design automation is suggested. The software implementation was done with Microsoft Visual Basic 6.0 and Access 2000, Heide Corporation Intent! for AutoCAD 2000, and AutoDesk AutoCAD 2000. q 2002 Elsevier Science Ltd. All rights reserved. Keywords: Pipe-routing; Design automation; Expert system; Cell generation method 1. Introduction Piping-system design for ships usually has five consecu- tive phases: preliminary design, functional design, detail design, production engineering, and system-support infor- mation (ISO/CDC, 1996). Pipe routing is the most important activity during the detail-design phase because it takes over 50% of the total detail-design man-hours and all other activities of detail design depend on it. Since detail design is characteristically less creative and more routine than the earlier steps in the design process (Kang, Myung, & Han, 1999), a computer program that has an automatic pipe- routing capability might offer an attractive way to cut down on dull and irksome work, leading to saving of time and money. Pipe routing has traditionally been done largely by eye (Wangdahl, Pollock, & Woodward, 1974), and develop- ing pipe-routing programs has been constrained by various considerations, such as: (a) obstacle avoidance (b) minimum equipment clearance requirements (c) accessibility of valves by hand or by reach-rods (d) maximization of support sharing with other pipelines (e) minimization of pipeline length and number of bends Although pipe-routing algorithms have already been developed, to date they have striven to accommodate mainly constraint (a) (Ito, 1999; Kobayashi, Wada, & Kiguchi, 1986; Schmidt-Traub, Koster, Holtkotter, & Nipper, 1998; Wangdahl et al., 1974; Zhu & Latombe, 1991), and have paid little or no attention to constraints (b)–(d). They have used the constraint (e) as an optimal criterion. In addition, even though pipelines without branches are uncommon, only Newell (1972) was concerned with branching pipelines. These design constraints can be divided into two groups: restrictive constraints (a) – (c), and quantifiable constraints (d)–(e). To satisfy restrictive constrains the proposed algorithm uses pattern-match techniques to provide some ‘good’ feasible paths for a given pipeline, generating cells so that a series of cells can lead a pipeline from a starting point (nozzle) to an end point (nozzle). On the other hand, for the quantifiable constrains, the algorithm assesses them from a fiscal point of view to find the best path among the set of feasible paths. 2. Routing algorithms 2.1. Maze algorithm Lee (1961) proposed a maze algorithm, also called ‘Lee’s algorithm’ or the ‘grid expansion algorithm’. This algorithm posits a grid of cells. Obstacles are marked ‘X’ on the cells corresponding to them. The algorithm begins from a starting cell S, and the cells adjoining that cell are labeled with a 0957-4174/02/$ - see front matter q 2002 Elsevier Science Ltd. All rights reserved. PII: S0957-4174(02)00049-0 Expert Systems with Applications 23 (2002) 299–309 www.elsevier.com/locate/eswa * Corresponding author. Tel.: þ 1-206-543-5348; fax: þ1-206-685-3072. E-mail address: [email protected] (J.-H. Park).

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  • Pipe-routing algorithm development:

    case study of a ship engine room design

    Jin-Hyung Park*, Richard L. Storch

    Industrial Engineering, University of Washington, Box 352650, Seattle, WA 98105, USA

    Abstract

    This study presents an automatic pipe-routing algorithm accommodating all major detail-design facets. First, the algorithm uses pattern-

    match methods to provide candidate paths. A cell-generation method is developed which satisfies geometric constraints. This makes the

    generation and evaluation of paths effective and efficient. Next, various non-geometric aspects, such as material costs, installation costs, and

    valve operability, are assessed from a fiscal point of view. Then, from a tree of combinations, the algorithm chooses an appropriate path for

    each pipeline from the candidate paths. Finally, a general approach toward detail design automation is suggested. The software

    implementation was done with Microsoft Visual Basic 6.0 and Access 2000, Heide Corporation Intent! for AutoCAD 2000, and AutoDesk

    AutoCAD 2000. q 2002 Elsevier Science Ltd. All rights reserved.

    Keywords: Pipe-routing; Design automation; Expert system; Cell generation method

    1. Introduction

    Piping-system design for ships usually has five consecu-

    tive phases: preliminary design, functional design, detail

    design, production engineering, and system-support infor-

    mation (ISO/CDC, 1996). Pipe routing is the most important

    activity during the detail-design phase because it takes over

    50% of the total detail-design man-hours and all other

    activities of detail design depend on it. Since detail design is

    characteristically less creative and more routine than the

    earlier steps in the design process (Kang, Myung, & Han,

    1999), a computer program that has an automatic pipe-

    routing capability might offer an attractive way to cut down

    on dull and irksome work, leading to saving of time and

    money. Pipe routing has traditionally been done largely by

    eye (Wangdahl, Pollock, & Woodward, 1974), and develop-

    ing pipe-routing programs has been constrained by various

    considerations, such as:

    (a) obstacle avoidance

    (b) minimum equipment clearance requirements

    (c) accessibility of valves by hand or by reach-rods

    (d) maximization of support sharing with other pipelines

    (e) minimization of pipeline length and number of bends

    Although pipe-routing algorithms have already been

    developed, to date they have striven to accommodate mainly

    constraint (a) (Ito, 1999; Kobayashi, Wada, & Kiguchi,

    1986; Schmidt-Traub, Koster, Holtkotter, & Nipper, 1998;

    Wangdahl et al., 1974; Zhu & Latombe, 1991), and have

    paid little or no attention to constraints (b)(d). They have

    used the constraint (e) as an optimal criterion. In addition,

    even though pipelines without branches are uncommon,

    only Newell (1972) was concerned with branching

    pipelines.

    These design constraints can be divided into two groups:

    restrictive constraints (a)(c), and quantifiable constraints

    (d)(e). To satisfy restrictive constrains the proposed

    algorithm uses pattern-match techniques to provide some

    good feasible paths for a given pipeline, generating cells

    so that a series of cells can lead a pipeline from a starting

    point (nozzle) to an end point (nozzle). On the other hand,

    for the quantifiable constrains, the algorithm assesses them

    from a fiscal point of view to find the best path among the set

    of feasible paths.

    2. Routing algorithms

    2.1. Maze algorithm

    Lee (1961) proposed a maze algorithm, also called Lees

    algorithm or the grid expansion algorithm. This algorithm

    posits a grid of cells. Obstacles are marked X on the cells

    corresponding to them. The algorithm begins from a starting

    cell S, and the cells adjoining that cell are labeled with a

    0957-4174/02/$ - see front matter q 2002 Elsevier Science Ltd. All rights reserved.

    PII: S0 95 7 -4 17 4 (0 2) 00 0 49 -0

    Expert Systems with Applications 23 (2002) 299309

    www.elsevier.com/locate/eswa

    * Corresponding author. Tel.: 1-206-543-5348; fax: 1-206-685-3072.E-mail address: [email protected] (J.-H. Park).

  • one. Cells adjoining these are labeled two. This process

    is continued until the target cell is reached (Rourke, 1975).

    This algorithm guarantees a solution, if one exists, but it

    requires a lot of memory space (Kai-jian & Hong-e, 1987).

    Mitsuta, Kobayashi, Wada, and Kiguchi (1986) and Rourke

    (1975) adopted this algorithm.

    2.2. Escape algorithm

    Hightower (1969) proposed an escape algorithm, otherwise

    called the line-search algorithm or the vector algorithm.

    This method starts with two perpendicular lines through the

    starting point S. It tries to find a point such that an escape line

    will extendbeyond one of the previousboundaries ofpointS. If

    such an escape point is found, it becomes the new point S. This

    method repeats the process until the line segment crosses the

    target point G. The escape algorithm is fast and uses less

    memory space, but it cannot guarantee a solution (Kai-jian &

    Hong-e, 1987). Schmidt-Traub et al. (1998) utilized both the

    maze and the escape methods to ascertain the optimal pipe

    route for plant layout.

    2.3. Network optimization algorithm

    Pipe-routing can be done using network optimization

    algorithms. Each vertex vi denotes a junction of possible

    pipe racks or pipe segments between junction i and j, and

    each edge eij has a cost cij:

    G V; E;where V is the set of vertices and E is the set of edges. Then

    the problem becomes one of finding the shortest path

    between a node origin s and a node destination t (Guiradello,

    1993). Wangdahl et al. (1974) and Guirardello (1993) used

    Dijkstars algorithm (Mandl, 1979). Newell (1972) adopted

    Nicholsons method (1966).

    2.4. Ap algorithm

    Zhu and Latombe (1991) developed an approach based

    on robot-path planning techniques, regarding each pipe as

    the trace of a rigid object, either a disc (2D case) or a sphere

    (3D case). This method used cell decomposition and

    connectivity graphs to find an appropriate path, and an Ap

    algorithm for optimality, which is:

    f N gN hN:The term g(N ) is a function of a weighted sum of a given

    length and its number of turns, and h(N ) is a function of the

    Manhattan distance between the center of the cell of node N

    and the terminal.

    2.5. Genetic algorithm

    Ito (1999) employed to a genetic algorithm (GA)

    approach to find the best path. Each cell is given a potential

    value, according to its situation. The potential value of cells

    located next to the wall is lowest because the path is more

    favorable, and the value for obstacle cells is correspond-

    ingly high. This method tests crossover between possible

    paths, excluding obstacle cells.

    3. Understanding constraints

    Obstacles. Detail-design pipe designers need not only

    system diagrams, but also many drawings from other

    designers for such things as structural design, machinery

    design, electrical system design, and HVAC system design.

    All of these restrict feasible areas. As the avoidance of

    obstacles is essential to routing, all research has been forced

    to address this constraint.

    Operability. As certain valves should be readily

    accessible during normal operation or easily accessible in

    an emergency, piping system designers want to locate these

    valves within arms reach. This has a direct effect on

    pipeline paths.

    Material costs. This is obviously one of the most

    important constraints. Material costs are tied to the length

    and the number of turns of each pipeline. Designers place

    the biggest pipelines first so as to avoid having bigger

    pipelines go around smaller ones. Bending or elbows are

    used to make right-angled turns, but bending is preferred

    because it is less costly. Elbows are substituted when there

    is not enough space for bending.

    Installation cost. A pipeline is associated with a series of

    Table 1

    Earlier studies on pipe routing

    Algorithm Network optimization Maze Escape GA

    Au thor Newell Wangdahl Guiradello Zhu Rourke Mitsuta Schmidt Ito

    Year 1972 1974 1993 1991 1975 1986 19 1999

    Dimension 3D 2D 3D 2D/3D 3D 3D 3D 2D

    Domain General Shipbuilding Plant Robotics General General Plant General

    Operation/Maintenance X X X X X X X X

    Installation X X P X X X P P

    Safety X X X X X F X X

    Branch P X X X X X X X

    X: not considered, P: partially implemented, F: fully implemented.

    J.-H. Park, R.L. Storch / Expert Systems with Applications 23 (2002) 299309300

  • supports. These may support other pipelines as well.

    Having a support hold two or more pipelines can make it

    possible to save material costs for the supports them-

    selves, and the man-hours required for the welding of

    support legs to the structure. Sometimes, the need to

    provide welders with good positions or the shortening of

    support legs so as to reduce vibration leads to changing or

    lengthening routes.

    Flow direction. Some pipelines, such as sewage

    pipelines, use gravity to create flows. Loops are not

    recommended for pipelines for steam or gas because of

    potential drainage problems.

    If a pipe-routing designer were to disregard a

    constraint, an acceptable outcome would be unlikely.

    Table 1, however, shows that earlier studies have ignored

    some important constraints. Since maze algorithms and

    network optimization algorithms have been developed to

    find the shortest path, studies using them might be limited

    at the outset in taking account of the whole gamut of

    constraints.

    4. Cell-generation method

    4.1. Background

    Many early studies on routing have been carried out

    either by means of cell-decomposition methods or by means

    of optimization network techniques. A major problem of

    cell-decomposition methods is the number of cells. As the

    number of cells increases, the difficulty in dealing with them

    grows exponentially. And the main difficulty of optimiz-

    ation network techniques is to define all nodes before an

    algorithm runs. Nodes are the places where pipelines are

    forked, joined, or turned. Anticipating locations for these

    nodes is also a demanding task. This suggests the need to

    develop a cell-generation method.

    A cell-generation method has been developed to satisfy

    geometric constraints, including interference, valve oper-

    ability, and safety. This method uniquely uses directional

    specifications to choose an appropriate basic pipe pattern. It

    also considers possible modified pipe patterns so as to

    enable a pipeline to run together with others. To make

    interference checking easier, each obstacle will be fitted

    with a cubic box large enough to contain it. If an obstacle

    requires a minimum clearance for safety concerns, the size

    of cube would increase accordingly. Before the algorithm

    proceeds to generate cells, it checks whether any obstacles

    are in the way.

    4.2. Directional specifications

    When designers start laying out pipes, equipment

    locations have already been determined. Since information

    on the start/end coordinates and the directional specifica-

    tions for a given pipeline is known, taking such factors into

    consideration makes pipe routing more feasible. For

    instance, as shown in Fig. 1(a), a pipeline should start

    from a tank nozzle ( p1) heading left and end up going in the

    same direction to complete a connection between a tank and

    a pump.

    Fig. 1(b) shows that without the specification of direction

    either path A or path B would look feasible. As seen in

    Fig. 1(c), however, the specification of direction at both

    ends rules out paths A and B of Fig. 1(b) and suggests,

    instead, a new path C. This is called a one-variable style

    throughout this paper because the dimension a will be

    established depending upon boundaries and other pipeline

    routes after the evaluation of all other possible routes.

    Fig. 1. Pipe routes with directional specification.

    Fig. 2. Cost comparison.

    J.-H. Park, R.L. Storch / Expert Systems with Applications 23 (2002) 299309 301

  • 4.3. Basic vs modified patterns

    Pipeline diameter may have the largest effect on pipe-

    routing. The bigger the pipeline diameter, the higher is its

    priority. Higher priority pipelines would be routed prefer-

    entially and have basic routing styles, which means

    minimizing length and turns. One the other hand, lower

    priority pipelines tend toward modified routing styles, with

    longer lengths and more turns permitted, in order to run

    together with other pipelines as much as possible and/or to

    avoid obstacles. Two or more pipelines running together can

    share pipe supports, leading to decreased installation costs.

    For instance, Fig. 2(a) has two pipelines that have the same

    basic pattern. The smaller diameter pipeline route can be

    modified as shown in Fig. 2(b), which results in a significant

    cost reduction.

    Depending upon end point and directional specifications,

    it is possible to recognize nine basic styles, as in Fig. 3. One-

    variable basic styles have one degree of freedom, and branch

    pipelines, pipeline length, and boundaries are factors for

    defining that freedom. Some detour styles, according to the

    nature of the obstacles are shown in Fig. 4.

    All pipelines have a basic style. To avoid failure

    occasioned by obstacles, pipelines can have a detour style

    instead of a basic style.

    When it is possible for a pipeline A to run together with a

    pipeline B, the algorithm provides appropriate connectors,

    like freeway access ramps, so that both ends of pipeline A,

    respectively, can access the established route cell which has

    been built for pipeline B. The top row of Fig. 5 shows

    possible ramp styles and the bottom row shows detour ramp

    styles.

    4.4. Generating cells

    Terminal cells. Based on directional specification and

    pipe diameter, the algorithm makes terminal cells. Each cell

    is a hexahedron and its size is dependent on pipe diameter.

    The center of the originating face is located at the endpoint

    of the pipeline and cells grow along the direction of the

    directional specification. If two or more terminal cells

    should touch each other, a new cell, big enough to hold

    them, will be generated. The old cells will be eliminated.

    Bridge cells for basic styles. The algorithm selects an

    appropriate basic style for each pipeline, given its endpoints

    and the directional specifications. Cells are generated in

    descending order of pipe diameters.

    Bridge cells for modified styles. Establishing one or more

    connections with modified styles is like accessing freeways

    from local roads. Bridge cells for basic styles serve as

    freeways, and generating bridge cells for modified styles

    corresponds to the construction of on/off ramps for

    freeways. All bridge cells will be endowed with information

    with regard to directly connected neighbor cells.

    An example. Fig. 6 is an example of cell generation, with

    three pipelines. First, generate six terminal cells at the

    endpoints (Fig. 6(b)). After merging two cells 10 and 20

    (Fig. 6(c)), generate a bridge cell 12 to connect the four-

    inch pipeline, which is the largest pipeline, because a

    Fig. 3. Basic routing styles.

    Fig. 4. Detour routing styles.

    J.-H. Park, R.L. Storch / Expert Systems with Applications 23 (2002) 299309302

  • Fig. 5. Ramp styles for modified routes.

    Fig. 6. An example: cell-generation procedure.

    J.-H. Park, R.L. Storch / Expert Systems with Applications 23 (2002) 299309 303

  • straight-line type is the basic pattern (Fig. 6(c)). Then,

    generate a bridge cell 22 for the two-inch pipeline because

    an L type is the basic pattern and the algorithm has already

    supplied cell 12 as part of the L (Fig. 6(d)). Generate two

    bridge cells 32 and 33 to form another L type connection

    (Fig. 6(e)). Once basic patterns have been built for each of

    the pipelines, the algorithm starts to try out modified

    connections. The four-inch pipeline does not get a modified

    connection because it is the largest pipeline. The two-inch

    pipeline has a basic style and cannot have a modified style in

    conjunction with the four-inch pipeline. But the two-inch

    pipeline can have a modified style in conjunction with the

    one-inch pipeline by generating 41 and 42 to access cell

    33 and by generating 43 to access cell 32 (Fig. 6(f)). At the

    same time, the one-inch pipeline has a modified style with a

    basic style of the two-inch pipeline using cells 41, 42, and

    43. Fig. 6(g) shows the final cells. The four-inch pipeline

    has only one possible route, and the two-inch and the one-

    inch pipelines each have two possible routes.

    4.5. Branches

    While arranging pipelines, the main difficulty in dealing

    with branches is uncertainty as to the physical location of

    branch point. To make this question manageable, the

    algorithm regards any branch pipeline as a compound of

    two simple forms: end-forked and middle-forked.

    End-forked form. This form has branches near one or

    both endpoints of a pipeline. The general steps to achieve

    proper routes are as follows. First, make a basic route

    between forked ends of one side. Then, define an imaginary

    endpoint on the route based on the system diagram and

    attribute possible directional specifications to the imaginary

    endpoint (Fig. 7(b)). Consider the prospective pipeline as a

    normal pipeline defined by the imaginary point and the

    endpoint of other side to be joined.

    Middle-forked form. Since decreases in the length of

    daughter pipelines and in their number of turns usually

    means increases in them of the mother pipeline, the

    comparison between mother pipeline losses and daughter

    pipelines gains should be carried out. When the mother

    pipeline losses are greater, branch pipelines should not exert

    an important effect upon the routing of the mother pipeline.

    Branch points are located where branch pipelines have a

    minimum length orthogonally with respect to mother

    pipelines.

    Recursively-forked form. Sometimes a branch pipeline

    has a daughter branch pipeline associated with it and the

    daughter, in turn, has a daughter, and so on, which may be

    called a recursively-forked form. This form might be a

    combination of subsets of an end-forked form and a middle-

    forked form. From the main pipeline, which is the largest

    and longest pipeline, try out combinations of end-forked

    forms and/or middle-forked forms recursively. Fig. 7(a) is a

    sample system diagram, representing a schematic map of

    pipelines and instrumentation. The pipeline from the

    Central Coolers to the Air Conditioning Plant is obviously

    the main pipeline with end-forked forms at both ends, and

    the smaller diameter pipeline to the M/E Air Coolers is a

    middle-forked form. Therefore, two different basic routing

    Fig. 7. Routing branch pipelines.

    J.-H. Park, R.L. Storch / Expert Systems with Applications 23 (2002) 299309304

  • styles can be built for the main pipeline Fig. 7(b). The

    pipeline to the M/E Air Coolers has an end-forked form and

    can be connected either to P001 or to P002 (Fig. 7(c)). Then,

    the three-inch pipeline can be treated as a main pipeline, in

    turn, and the two-inch pipeline is a middle-fork form (Fig.

    7(d)).

    5. Evaluation

    Since the main factors in the evaluation of pipelines have

    to do with material cost, installation cost, and operability

    (valve locations), a penalty function can be developed by

    combining these factors based on cost;

    Ppenalty function material cost installation cost accessibility cost

    5.1. Material cost and installation cost

    The raw material cost of pipe and elbows depends on

    pipe size and length. The bending cost follows a step

    function because cold bending is used for small pipes and

    high frequency bending is used for large pipes (Fig. 8). The

    bigger the pipeline diameter, the more costly the bending,

    because it takes more man-hours. As for installation cost, it

    is directly related to pipe-support costs, for pipelines require

    a series of pipe-supports. Pipe-support cost is comprised of

    the costs for raw materials, paint, transportation, welding,

    and u-bolts. The distance between supports varies in

    accordance with the pipe size and the particularities of the

    underlying steel structure.

    5.2. Operability cost

    The last term in the penalty function, operability cost,

    can be derived from the following formula:

    O Co TotalHoursOfOperation HourlyWageOperability coefficient Co. The operability coefficient Co

    reflects the difficulties and discomfort inherent in operating

    the valves. Difficulty is taken as proportional to handwheel

    diameter. As for discomfort, the ABS guidelines (1998)

    plotted in Fig. 9 show that the first choice is better than the

    second choice and much better than the third choice.

    Research by Karwowski and Salvendy (1998) found a linear

    relationship between gravitational load and body discomfort

    as shown in Fig. 10.

    The numbers in the bars are the degree of comfort. The

    degree of comfort for operating in the first choice zone can

    be averaged as 2.65 and that for the second choice would be

    0.65. In other words, the second choice is 3.12 times more

    uncomfortable than the first choice, while the third choice is

    7.57 times more uncomfortable. Table 2 shows the

    operability coefficients. The underlying assumption is that

    the operation of the valves for an eight-in pipe is taken as a

    norm and assigned a value of 1. When valves are located

    within the same zone with regard to a standing body, the

    coefficient varies with handwheel diameter. Coefficients for

    the second choice zone are derived by multiplying the first

    choice coefficients by 3.12.

    Total hours of operation. This factor is given as the

    TimeRequiredToOperateEachTime times TotalNumOf

    Operation. The term TotalNumOfOperation means the

    total number of operations through a valves lifetime and

    Fig. 8. Pipe size and bending cost comparison.

    Fig. 9. Mounting heights for handwheel valves with stem vertical.

    Table 2

    Operability coefficientsstem vertical case

    Pipe size (inch) First choice Second choice Third choice

    2 0.25 0.78 1.89

    3 0.38 1.17 2.84

    4 0.50 1.56 3.79

    6 0.75 2.34 5.68

    8 1.00 3.12 7.57

    10 1.25 3.90 9.46

    12 1.50 4.68 11.36

    14 1.75 5.46 13.25

    16 2.00 6.24 15.14

    18 2.25 7.02 17.03

    20 2.50 7.80 18.93

    24 3.00 9.36 22.71

    J.-H. Park, R.L. Storch / Expert Systems with Applications 23 (2002) 299309 305

  • differs widely from pipeline to pipeline. On a ship, for

    instance, valves for the bilge system are operated frequently,

    both when the ship is at anchor and at sea. On the other

    hand, valves for the return-line system are always open and

    those for the ballast system are usually automatic. So the

    total number of operations for these valves is relatively low.

    5.3. Tree of combinations

    The tree of combinations is to help visualize the

    schematic relationship among all candidate pipelines.

    From Fig. 11, (A1, B1, C1, D1) and (A1, B2, C3, D2) are

    possible combinations. As (A1, B2, C1, D1) always

    dominates, which means always less costly than, (A1, B2,

    C2, D1), (A1, B2, C2, D1) is eliminated from the tree.

    Blocking is another cause for elimination. An example is the

    combination (A1, B1, C3, D1), where B1 blocks C3.

    A unique characteristic of this tree is that the higher the

    level the bigger the assigned value because the algorithm

    starts from the largest pipeline. Consider a node N1 and its

    sibling node N2. When the N1 costs are so much lower

    than the N2 costs that the difference between the best case

    of the N2 descendant nodes and the worst case of N1

    descendant nodes could not be made up, the node N2 and

    its dependents can be deleted. In Fig. 12, as B2 is low

    enough, B1, B3 and their dependents are pruned.

    Similarly, the algorithm deletes C3 and D2.

    6. Implementation

    6.1. Input data

    The input data, mainly from system diagrams and

    equipment arrangement drawings, has to do with bound-

    aries, equipment, preferred operation area for valves, and

    pipelines to be routed. Information on pipelines consists of

    the pipeline number, diameter, directional specification, the

    types and coordinates of endpoints, and branch pipeline

    number. Daughter pipelines are given the mother pipeline

    number, instead of an endpoint type.

    Fig. 10. Ranking of postures, based on maximum holding time (MHT).

    Fig. 11. Tree of combinations.

    J.-H. Park, R.L. Storch / Expert Systems with Applications 23 (2002) 299309306

  • 6.2. Data structure

    Fig. 13 shows a data structure for any given pipeline. A

    pipeline has a series of nodes as endpoints, branch points,

    and valve points. Whenever a pipelines make a turn, a new

    node will be dynamically generated and endowed with an

    elbow or bending specifications. Table 3 lists all node types.

    As seen in Fig. 14, any given cell knows which pipelines

    go through that cell and which cells are connecting to it for

    ease in pipeline route-tracking. Each cell has as many slots

    as pipelines going through that cell, and slots are used for

    physical pipeline routing. Touching between slots, there-

    fore, should be avoided. Given locations of boundaries and

    confirmed slots, it is possible to choose an adequate pipe-

    support type for slots yet to be determined.

    6.3. Implementation

    The algorithm and GUIs have been developed with

    Microsoft Visual Basic 6.0. Microsoft Access 2000 is used

    for data about pipe costs, elbow costs, and bending costs

    because they are subject to change.

    The final values from Visual Basic are transmitted to

    AutoCAD 2000 for visualization through Intent! For

    AutoCAD 2000. Fig. 15 is the output produced by the

    algorithm, which has fifteen pipelines. A pipeline from A1

    to A2 is an instance of modified style.

    7. A generalization of detail-design automation method

    Design can be viewed as a constraint-satisfaction process

    (Yoon, 1992). If all constraints are quantifiable, optimiz-

    ation techniques can be introduced into the design process to

    find the most appropriate alternative (Radford & Greo,

    1988). Design constraints, however, especially in the detail-

    design phase, lack comprehensive formulations (Yoon,

    1992). To overcome this limitation, it is possible to produce

    some good-feasible solutions, such as to satisfy restrictive

    constraints, by pattern-match on a case-by-case basis. This

    narrows down the range of possible solutions to a

    Fig. 12. Pruning tree branches.

    Fig. 13. Data structure of pipeline.

    Table 3

    Pipe node types

    Node type Description

    2 Start point, known

    10 Start point with valve, known

    14 End point, known

    20 End point with valve, known

    21 Branch start point, not determined

    22 Branch start point, determined

    23 Branch end point, not determined

    24 Branch end point, determined

    30 Branch end point with valve, determined

    35 Middle point for valve, not determined

    40 Middle point for valve, determined

    55 Middle point for daughter pipeline, not determined

    56 Middle point for daughter pipeline, determined

    65 Middle point for turn, type not determined

    66 Middle point for bending, determined

    68 Middle point for elbow, determined

    J.-H. Park, R.L. Storch / Expert Systems with Applications 23 (2002) 299309 307

  • manageable size. Then, quantifiable constraints are

    exploited to find the most beneficial solution. Fig. 16 is a

    representation of the success stages of this process.

    8. Conclusion

    In this paper, a cell-generation driven pipe-routing

    method has been developed to supply candidate paths.

    From a tree of combinations, the algorithm chooses an

    appropriate path for each pipeline from the candidate paths

    after various non-geometric aspects, such as material costs,

    installation costs, and valve operability, are assessed from a

    fiscal point of view.

    The methodology showing how various design con-

    straints might be accommodated by suitable algorithms can

    be a cornerstone for the evolution of design automation for a

    domain fraught with complications. The algorithm pre-

    sented here is for pipe routing. Further research will look

    into applications of this algorithm for electric harness and

    HVAC ducts.

    Acknowledgments

    The authors would like to acknowledge David Evans,

    Lawrence Pierce, and Randy Bird of Integration Partners,

    Inc., San Diego, CA, for providing software and valuable

    technical advice. They also would like to thank pipe-routing

    experts, Jung-Hyun Park, Jin-Soo Youn, Young-Min Kang,

    Pil-Joong Nam and Woo-Jong Kim of Samsung Heavy

    Industries, Korea, for indispensable advice.

    Fig. 14. Data structure of cell.

    Fig. 15. Pipe-routing using the algorithm.

    Fig. 16. Constraints-based design automation.

    J.-H. Park, R.L. Storch / Expert Systems with Applications 23 (2002) 299309308

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    Jin Hyung Park is a PhD candidate in Industrial Engineering at the

    University of Washington of the USA. He worked in the shipbuilding

    industry for five years before he received his Masters degree at KAIST

    in Korea in 1997.

    Richard Lee Storch is a professor of Industrial Engineering at the

    University of Washington. His research has concentrated on pro-

    ductivity and quality improvement in ship production and large

    assembly manufacturing systems. He is a member of SNAME, IFIP

    WG 5.7, IIE, and ASNE. He serves on the editorial boards of the

    Journal of Ship Production and the Journal of Marine Science and

    Technology.

    J.-H. Park, R.L. Storch / Expert Systems with Applications 23 (2002) 299309 309

    Pipe-routing algorithm development: case study of a ship engine room designIntroductionRouting algorithmsMaze algorithmEscape algorithmNetwork optimization algorithmA* algorithmGenetic algorithm

    Understanding constraintsCell-generation methodBackgroundDirectional specificationsBasic vs modified patternsGenerating cellsBranches

    EvaluationMaterial cost and installation costOperability costTree of combinations

    ImplementationInput dataData structureImplementation

    A generalization of detail-design automation methodConclusionAcknowledgmentsReferences