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Intelligent Ship Arrangements: A New Approach to General

Arrangements

Ambattuparambil Gopi Nikhil

Roll Number: 09NA1008

Department of Ocean Engineering & Naval Architecture

Email: agnikhil@iitkgp.ac.in

Abstract

A new surface ship General Arrangement optimization system is described in this paper. This system

envisions assisting the arrangements designer in developing arrangements that satisfy the design

needs as well as owner requirements to the maximum extent practicable. The arrangement process is

approached as two essentially two-dimensional tasks. First, the spaces are allocated to Zone-decks,

one deck in one vertical zone, on the ship’s inboard profile. Then the assigned spaces are arranged in

detail on the deck plan of each Zone-deck in succession. Consideration is given to overall location,

adjacency, separation, access, area requirements, area utilization, and compartment shape. The

system architecture is quite general to facilitate its evolution to address additional design issues, such

as distributive system design, in the future.

Introduction & Problem Definition

The General Arrangement (GA) of a ship is a layout of the interior of the ship showing its main

elements and components like arrangement of passenger and crew accommodation, machinery

rooms, ballast and fuel oil tanks, stores, cranes, holds and engineering. The GA of the ship

demonstrates how the Naval Architect has addressed the needs and requirements of the

owner/operator.

The manual version of creating the General Arrangement of a ship is daunting task because of the

need to consider many conflicting goals, requirements and constraints. All shipyards employ

considerable amount of high-paying Naval Architects in order to achieve a good GA. GA design may

still have to be done even if that type of ship has already been built by the same shipyard. Other

features that makes GA design difficult is the non-uniformity between the layout, facilities and

conditions in different shipyards. Moreover, owners and operators almost never have the same

requirements for two different ships. Since, the competition in shipbuilding is cut-throat, the

requirement to try and reduce any cost while still getting the same output is what is important and

necessary.

The objective of the Intelligent Ship Arrangements (ISA) system is to provide maximum intelligent

support to the arrangements designer in making optimum and effective designs and thus, reducing his

effort and time.

The system in question would have the following features in order to reduce the efforts of the

designer:

Highly flexible to account for variations in requirements and constraints of various ship types

Ability to capture and invoke standard requirements and best case practices

Introduction of a measure of utility to compare different arrangements

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Approach to Problem Solving

The arrangement problem has been approached in two essentially different parts:

1. Spaces are allocated to Zone-decks on the ship’s profile. A Zone-deck is defined as one deck

within one vertical zone as shown in Figure 2. On the Damage Control deck, where decks are

divided by longitudinal passages, there are 3 sub-Zone-decks – port, centre and starboard.

The relative importance of each space is considered in this stage.

2. The assigned spaces are arranged on the deck plan in a priority order starting from the

middle of the Damage Control deck. At this stage, area requirement, adjacency, separation,

access and shape of individual spaces are considered and accordingly defined.

Usage of Soft Computing Tools

The optimization of surface ship genera arrangement is a challenging and complex problem

characterized by a large search space and a high number of conflicting goals and constraints.

Fuzzy Optimization

In the arrangements problem, a lot of the design

goals and constraints are subjective in nature

and thus a fuzzy optimization model is used to

allocate the fuzzy utilities for each

goal/constraint. The fuzzy membership function,

0 ≤ U(x) ≤ 1, is used. A typical U(x) vs. x graph

that would be used is shown in figure 3. When

U(x) = 0, the design is completely unacceptable

for the designer and when U(x) = 1, the design is fully acceptable.

Allocation Optimization

Design variables x

The unknown design vector x is the assignment of each space i = 1, 2,…, I to one of the Zone-decks k

= 1, 2,…, K as follows:

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(ii)

[ ] Thus, a 3 in the second entry (x2) would assign 2 to Zone-deck 3. The chromosome to be used for

optimization using Genetic Algorithm (which is usually binary) is taken as an integer (decimal).

Zone-deck area utilization utility

In order to decide on the utility of a space for a

particular use, fuzzy membership functions are

utilized such that 0 ≤ Uk ≤ 1. A normal distribution is

used with under-utilization to have a higher

preference over over-utilization (crowded spaces).

Also, it may be that the utility function has a plateau

of Uk = 1 in between the two distributions of under

and over-utilization. Figure 4 shows a normal

distribution graphically.

Global location goals

Most other goals and constraints are discrete in

nature with certain spaces being utilized for

certain specific requirements. These

requirements may arise from classification rules,

owner/operator requirements and/or optimum

design requirements. Whatever the case, they

need to be satisfied with high utility values. An

example is given in Figure 5 where the Damage Control deck’s utility is satisfied at the blue region

(1.00) and in the green region (0.50), albeit with lower utility. Another example might be the

requirement of the engine to be on the aft end of the ship. The space next to the aft peak bulkhead

would thus, have a membership value of 1.00 there.

Adjacency/separation constraints

Some spaces need to be adjacent to a certain other

space. These requirements are needed in spaces like

the control room, which needs to be near the

accommodation region. Certain other spaces need to

be far away from certain others. For example, in

offshore structures, the flares are usually away from

the accommodation region. These constraints are met

as shown in Figure 6.

Overall allocation utility

Taking all these utilities into consideration, a final utility function is derived integrating all the above

features as follows:

[ ]

∑

∑

∑

[ ]

U1 would seek to raise the lowest Zone-deck utilization utility. U2 seeks to raise the average of all the

utilities while U3 raises the weighted average. The weights 0 ≤ wi ≤ 20, express the relative importance

of each of the spaces.

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Hybrid agent/GA algorithm

The allocation utility gives a means to approach the optimization problem by formulating a

mathematical function to measure the utility of an arrangement. The optimization problem can be

solved by trying to achieve the maximum utility. An agent approach is proposed in the paper in order

achieve the optimum arrangement. An agent is an element of code/object that behaves in a particular

manner. A group of agents working in parallel can achieve at the optimum solution much faster than a

normal GA approach.

Example Allocation

An example vessel is used to demonstrate the allocation of different spaces. The vessel used was a

3150t, 109m Corvette design. Combat spaces, superstructure deck and an engine room were

eliminated and were optimized for habitable spaces.

However, even with these eliminations and a variety of fixed

areas like the machinery rooms (engine & generators),

equipment rooms, anchoring, mooring, electrical equipment

and control rooms, there were 1307 goals and constraints. A

population of 10 was run for 1500 generations. Also, the

Zone-deck utilization utility curve neglected the penalty on

under-utilization by using a plateau from zero to 0.90 or 0.95.

The resultant fitness (total utility U) is shown in Figure 7. The

best solution was reached in 181 generations in about 20

minutes. The program was still run for 1500 generations to

ensure that the solution hadn’t settled for a local maxima. A

total utility of 0.778 was achieved. The minimum utility of a

space was 0.30. The fact that all spaces were not able to achieve a utility of 1.00 is evidence of the

high degree of compromise required in General Arrangement design.

Conclusion

The Intelligent Ship Arrangements optimization algorithms and systems provide an efficient and fast

means of obtaining a GA. It guarantees the optimum nature of the GA as well as gives output in a

much faster means. What used to take days and months to obtain in a shipyard’s design department,

would take hours. The process would still remain under the control of the arrangements designer,

however, because he knows best about the requirements of the owner and the feasibility and access

of each space. Also, he is required to express the design needs and construct the fuzzy constraints

and goals.

Future Scope

The use of Intelligent Ship Arrangements has great scope in terms of obtaining the GA for various

ships. After obtaining the GAs for different classes and types of ships, Neural Networks can be used

to obtain a code that would give an optimum design with minimum input.

Also, another scope would be to extend the arrangements design into its earlier stage, the Hull

Design, which would require optimum 3D geometry formation, optimum propeller and rudder design

with constraints that would include minimum resistance using laminar waterlines, maximum

volume/cargo holding capacity, required form coefficients according to ship type and no

trim/heel/ballast requirements.

Intelligent Ship Arrangements showed that 3D spaces can be optimized using fuzzy logic and genetic

algorithms. Hull Design can probably be optimized by using fuzzy functions that measures merit

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depending on the coordinates of the nearby points of the waterline. Higher membership values would

have to be given to features like bulbous bows, sterns, low slamming probability, high passenger

comfort, etc.

The main challenge lies in converting these features into mathematical functions. This could be

achieved by taking an initial ship hull and having functions that would simulate these features.

Weights could be given to each of those functions and values of coordinates can be changed

according to the optimization procedure. Since the functions will be huge in number, a hybrid agent

approach could be an answer.

References

Parsons M. G., Chung H., Nick E., Daniels A., Liu S., Patel J. (2008). Intelligent Ship Arrangements: A

New Approach to General Arrangement. Naval Engineers Journal, 120, 3, 51-65.

Daniels A. S., Parsons M. G. (2006). An Agent-based Approach to Space Allocation in General

Arrangements. 9th International Marine Design Conference, Ann Arbor, Michigan, U.S.A.