sd 1.1.2 ship dimensioning

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1

Ship Design I

Manuel [email protected]

MSc in Naval Architecture and Marine Engineering

M.Ventura Ship Dimensioning 2

Summary

• Ship Dimensioning

• Owner’s Requirements

• Traditional approach

• Generic Ship Dimensioning Process

• Most common implementation methods:

– Systematic parametric variation– Optimization methods

• Some Optimization Software Tools– Excel Solver

– Matlab fmincon() function

Annex A. Ships Statistical Data Gathering and Processing

Annex B. Physical Limitations to ship dimensions

Annex C. Economical Measures of Merit



2

Ship Dimensioning


Preliminary Design Process

Yang & al (2006)

The determinationof the main

dimensions andcharacteristics ofthe ship is the firststep of thepreliminary designstage.



3


Owner’s Requirements

Example of requirements:

Type of ship: Container-carrier, with cell guides

Mission: Service Line Setubal - Antwerp

Deadweight: 9,500 dwt

Max. Draught: 8.0 m

Cargo capacity: 750 TEU, including 20 reefers

Service speed: 17 knots

Autonomy: 20,000 miles

Cargo Equipment: 2 cranes of 40 t x 26.5 m

Other: Accommodations for 15 people

The starting point is a set of Owner’s requirements definingmainly the ship type, cargo capacity and speed


Ship Dimensioning – Traditional Process

• DW (input)

• Assumed a (DW/ Displacement) ratio empirically

• Displacement = DW / (DW/ Displacement)

• Lpp = f (Displacement, Vs )

• Cb = f ( Fn, Displacement, Vs )• B, T, D are functions of:

– Space requirements (cargo and ballast volumes, max.dimensions)

– Intact stability

– Free Board

– Reserve of flotation



4


Selection of the Form Coefficients• Selection of the Cb

– In diagrams similar to the one in the figure, as a function of the FroudeNumber


Selection of the Main Dimensions



5


Ship Initial Dimensions• Watson and Gilfillan (1976) presented the following

procedure to obtain the main dimensions of a ship with therequired displacement ∆

( ) ( )( )

3

1

2

1025.1 ⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

⋅+⋅

⋅⋅Δ=

BC s

T B

B L

L

( )

( )( )

T DT D

T B

BT

B L

L B

⋅=

=

=

• The ratios (L/B), (B/T)and Cb are obtainedfrom statistical data ofsimilar ships

• (D/T) is initially assumedas 1.20

• (1+s) is a coefficientrelated to the hullappendages


Generic ShipDimensioning

Process

Modern approach



6


Ship Model

Lpp, B, D, T, Cb

Etc.

Type of Propulsion System

Specific Fuel Oil Consumption

Etc.

Displacement

Cm, Cwl, Kb, Lcb, BMT, BML, SwLightship Weight, Kg, Lcg

GMtDW, CDWCargo Capacity

Ballast Capacity

Propulsion Power

Length of Engine RoomLength of Cargo Area

Etc.

Ship Model

Design

Variables

Possible

Solution

Technical

DesignParameters

Mission

Requirements

Type of ShipCDW, TEU, Lane Length

Vs

Autonomy

Etc.


Size Measures for Specific Ship Types

• Weight based design (Ex.: Tankers, bulk-carriers,..)– Cargo capacity depends mainly of the displacement

– Homogeneous cargoes

– CDW is the measure of cargo capacity

– Depth = f(Vcargo)

• Volume based design (Ex.: Container carriers)– Unitized or packed cargo

– Number of TEU is the measure of cargo capacity

• Area based design (Ex.: Ro/Ro ships)– Lane length for vehicle stowage is the most common measure of

cargo capacity



7


Constraints


Economical Assessment



8


Economical Measures of Merit• Initial ship cost

– The initial ship cost is not by itself a good indicator, somedesign options only become economically advantageous on thelong run

• Other criteria can be used to take into consideration therunning costs of the ship along its entire operational life

• The most common are:

– Required Freight Rate (RFR)

– Present Value (PV)– Internal Rate of Return (IRR)

• To evaluate these criteria the knowledge of the typical shipvoyage is required


Typical Voyage

• The specification of the typical ship voyage allows a morecomprehensive analysis of the economic aspects

• It may include:

– The number of ports visited during the round trip

– The distance between ports

– The cargo-handling capabilities available and the correspondinghandling rates and costs

– Port fees and taxes



9


Example of a Typical Voyage

SpecificationItinerário

1

23

Carga

1-2

2-3

Ritmos de carga/descarga

12

Termos de carga/descarga

Custos portuários do navio

1

2Custos portuários da carga

1

2

Frete

Setúbal

AntuérpiaSines

600 teu’s x 14 t

400 teu’s x 16 t + 200 teu’s vazios

60 teu’s/hora shinc70 teu’s/hora shinc

Li-Lo

€10,000 + 0.5xGT

€30,000 + 0.5xGT

€100/teu cheio, € 50/teu vazio

€120/teu cheio, € 70/teu vazio

RFR (frete mínimo requerido)


Some Common Freight Conditions

• fio (free in and out )

• fiost (free in and out stowed and trimmed )

• li-lo (liner in and liner out )

• shinc (Sundays and holidays included )

• sshex (Saturdays, Sundays and holidays excluded )



10


Other Generic Requirements

Ship Registry (conventional flag/convenience flag) MAR

Duration of the Investment (ship economic lifetime) 20 years

Capital Interest Rate (bank loans) 10%

Working days /year (Off hire days/year) 355 d (10 d)


Generation Engine for Design Variables



11


Determination of the Design Variables• Parametric Studies

– The independent variables are obtained by variation betweenthe lower and upper limits assumed

– Require more computing time when the number of designvariables is high

– No guarantees that the solution found is the optimal

• Optimization Methods

– The independent variables are obtained from an optimization

algorithm

– Possible to find a better and faster solution

– Only provides information about the optimal point found (singleobjective methods)

Parametric Studies



12


Parametric Study Methodology

• System of 5 equations• 11 variables• 3 variables fixed based on

the Owner requirements(DW, CCAP, V)

Experience shows that these three relations,which are relatively stable for each ship type,

are suited for a good initial estimateIntroducing these additional three relations, the solution ofthe displacement equation it is transformed in the solution ofsystem of eight non-linear equations.

( )

( )

( )

, , , , ,

, , , ,

, , , ,

WT

WT MCR

MCR

MCR CAP

L B T Cb

L DW

L f L B D T Cb P

P f L B T Cb V

D f L B Cb P C

γ Δ = ⋅ ⋅ ⋅ ⋅

Δ = +

=

=

=


FunctionalDiagram of theDimensioning by

Systematic

Variation

The system of non-linear equations issolved by an iterativeprocess



13


Main Dimensions• The main dimension can be obtained from the ratios and

coefficients used as independent variables

• For example:

23

1 DW

k

T L B

Cb B T

B B T T

L L B

B

γ

⎛ ⎞Δ = ⋅⎜ ⎟

⎝ ⎠

Δ=

⎛ ⎞ ⎛ ⎞⋅ ⋅ ⋅⎜ ⎟ ⎜ ⎟

⎝ ⎠ ⎝ ⎠

⎛ ⎞= ⋅ ⎜ ⎟⎝ ⎠

⎛ ⎞= ⋅ ⎜ ⎟

⎝ ⎠

DW k

⎛ ⎞= ⎜ ⎟Δ⎝ ⎠

The ratio k can be obtainedfrom statistics:

Optimization Methods



14


Types of Optimization Problems• Single-Objective

– Simplified process in which one only objective, considered themost important, is selected

• Multi-Objective– Closer to the reality– Several objectives can be in conflict between them

• Hybrid– A multi-objective problem is transformed into a single

objective– One of the objectives is selected as the most important andthe other are converted into a set of constraints that arevaried parametrically


Types of Methods and Algorithms

Linear Methods:• Linear Programming (LP)

• Newton

Non-Linear Methods:

• Gauss-Newton• Levenberg-Marquardt

• Sequential Quadratic Programming (SQP)

• Artificial Neural Networks (ANN)

• Genetic Algorithms (GA)

• Simulated Annealing (SA)

• Particle Swarm Optimization (PSO)



15


Non-Linear Methods (1)• Linear Successive Approximations

– The process starts from an initial feasible point– The functions are expanded in Taylor series around the initial

point, considering only the linear terms– The constrains and the objective functions are linearized in a

similar way and the problem is solved as linear.

• Random Search– The values of the design variables are generated randomly

between the lower and upper limits.– The values that do not comply to the constrains are eliminated

and are not used in the next functions.

– The process stops when all the variables comply to the criteriadefined.

• Direct Search– The process starts from an initial point and generates a

sequence of point that converge to an optimal point where thefunction has a minimum.


Non-Linear Methods (2)

• Advantages/Disadvantages

– Linear Successive Approximations – Fast process but wherethe non-linear behavior of the relations is lost due to thelinearization of the initial stage.

– Random Search – Slow process where the optimum point can bemissed due to the contraction process. It can be applied tomulti-modal functions.

– Direct search – Based on local search and global movements.



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Types of Optimization MethodsGlobal

• Is able to search through the entire design space to findthe optimal solution

Local

• Can converge to a local solution, missing possible solutions inother regions of the design space

Some Optimization Software Tools:Excel Solver



17


EXCEL Solver (1)• Available algorithms:

– LP - Linear Programming (assumed only if selected in <Options>)

– Non-Linear Programming (assumed by default)

• GRG2 - Generalized Reduced Gradient (Lasdom et al, 1998)

• The Solver approximates the Jacobian matrix (partialderivatives) using finite differences and re-evaluates it atthe beginning of each iteration

• Limits– 1 objective (Single Objective algorithm)

– 200 variables

– 100 implicit restrictions

– 400 simple restrictions (upper/lower limits)

• Usage:– <Tools>/<Solver>


EXCEL Solver (2)

• <Target cell> is the one where the objective function is evaluated

• <Equal To> define the type of problem (maximize, minimize, equal to)

• <Changing Cells> are the ones that contain the design variables (to beoptimized) and must be all in the active sheet

• <Constrains> list the constraints to be applied

Multiple range<Changing Cells> can beindicated separated bycommas:

$C$4, $C$6:$C$8



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Example: Bulk Carrier Dimensioning• The simplified Model used on the example is based on the

one presented in Xuebin (2009)

Objective Function


Example: Problem Constraints

• The following set of 14 constraints is applied:



19


Example: Bulk Carrier Dimensioning


Notes on the Spreadsheet Design (1)

• In order to make the model formulas more readable andeasy to debug and maintain, cell and range names should beused instead of just references

• Cell names are created by:

<Insert/Name/Define>

• The use of cell names avoids the need to use absolute cellreferences (Example: ‘Lpp’ instead of C$4$)

• Define all the cell names BEFORE entering the formulas

• Define explicitly the units of all the values



20


Notes on the Spreadsheet Design (2)• Constraints associated to intervals must be split in two.

Example:

25000 <= DW <= 500000

To be split into:

DW >= 25000

DW <= 50000

• Use color codes to identify the different types of cells. Forexample:

– <yellow> input cells

– <orange> constraints

– <red> objective function

– <gray> values computed by the model



• Container Carriers have dimensions external (breadth of ship) andinternal (inner breadth of cargo hold) multiple of the width of thestandard container (8.0ft = 2.44m)

• These conditions can be converted into additional constraints

• For example for the Breadth of the ship:Module(B/2.46) < 0.01

NOTES:

• The value 2.46 results from taking into consideration the width ofthe container plus the interval between containers (abt. 25 mm)

• In Excel the expression will be:

mod(B; 2.46)

where the function mod(a;b) returns the remainder of the divisiona/b



21


Notes on the Spreadsheet Design (3)• Sometimes it is convenient to be able to use more than one

objective in the optimization process

• Although the <Solver> is a single objective method, multipleobjectives can be taken into consideration by creating an objectivefunction which is the result of a weighted sum of severalcontributions:

Fobj = w0xF0 + w1xF1 + w2xF2 …

• The weights wi will be assigned by the designer in accordance tothe relative importance of each contribution and their sum will bealways equal to 1.0:

w0 + w1 + w2 + … = 1.0

• The sign of each weight will be positive, if the corresponding

contribution is to me minimized, or negative, if it is to bemaximized

• It is convenient to scale the different contributions to the sameorder of magnitude. For example each contribution can be scaled tobe in the interval [0, 1]



• The initial values for the variable cells should berepresentative of the values expected at the optimalsolution, rather than arbitrary values such as all zeroes.

• The Excel Solver is a local optimizer -> different sets ofinitial variables values should be tested to check the

consistency of the results and to help to find a globaloptimum

• The process can be made automatic by creating a macro todefine the design variables, the constraints and theobjective function and to run the Solver

• The macro can be converted into a VBA (Visual Basic forApplications) function



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VBA Programming in Excel• The first draft of a

program can be obtainedby recording a sequenceof commands (macro)using the macrorecorder:

<Tools/Macro/RecordNew Macro>

• Next the macro code can be run and edited in the VBA Editor<Tools/Macro/Macros/Run> or /Edit>

• The code should be extensively commented in order to make itsdebugging and maintenance easier


VBA Function for Dimensioning (1)

Sub OptimumShip()'' OptimumShip Macro' Macro recorded 2010-09-22 by Manuel Ventura'' Keyboard Shortcut: Ctrl+Shift+S'

' Clear Solver optionsSolverReset

' Minimize Objective FunctionSolverOk SetCell:="$K$17", MaxMinVal:=2, ValueOf:="0", _

ByChange:="$C$4:$C$9“

' Constraints' Lpp <=SolverAdd CellRef:="$C$4", Relation:=1, FormulaText:="$C$24"



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VBA Function for Dimensioning (2)' T <=SolverAdd CellRef:="$C$7", Relation:=1, FormulaText:="$C$25“' T <=SolverAdd CellRef:="$C$7", Relation:=1, FormulaText:="$C$26"' L/B >=SolverAdd CellRef:="$C$11", Relation:=3, FormulaText:="$C$27"' L/D <=SolverAdd CellRef:="$C$12", Relation:=1, FormulaText:="$C$28"' L/T <=SolverAdd CellRef:="$C$13", Relation:=1, FormulaText:="$C$29"' Cb >=SolverAdd CellRef:="$C$8", Relation:=3, FormulaText:="$C$30"

' Cb <=SolverAdd CellRef:="$C$8", Relation:=1, FormulaText:="$C$31"' Fn <=SolverAdd CellRef:="$G$15", Relation:=1, FormulaText:="$C$32"


VBA Function for Dimensioning (3)

' GMT >=

SolverAdd CellRef:="$G$31", Relation:=3, FormulaText:="$C$33"

' DW >=

SolverAdd CellRef:="$G$20", Relation:=3, FormulaText:="$C$34“

' DW <=

SolverAdd CellRef:="$G$20", Relation:=1, FormulaText:="$C$35"

' Vs >=SolverAdd CellRef:="$C$9", Relation:=3, FormulaText:="$C$36“

' Vs <=

SolverAdd CellRef:="$C$9", Relation:=1, FormulaText:="$C$37"

' Run Solver and

‘ allow the user to decide to keep or not the obtained result

SolverSolve userFinish:=False

End Sub



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Solver Options (1)• The Solver can be fine-tuned by changing the default

options


Solver Options (2)

• The <Max Time> and the <Iterations> edit boxes control theSolver’s running time.

• The <Show Iteration Results> check box instructs theSolver to pause after each major iteration and display thecurrent "trial solution" on the spreadsheet. In alternative

the user can simply press the ESC key at any time tointerrupt the Solver, inspect the current iterate, and decidewhether to continue or to stop.

• The <Assume Linear Model> check box determines whetherthe simplex method or the GRG2 nonlinear programmingalgorithm will be used to solve the problem.

• The <Use Automatic Scaling> check box causes the model tobe rescaled internally before solution.



25


Solver Options (3)• The <Assume Non-Negative> check box places lower bounds

of zero on any decision variables that do not have explicitbounds in the <Constraints> list box.

• The <Precision> edit box is used by all of the optimizers andindicates the tolerance within which constraints areconsidered binding and variables are considered integral inmixed integer programming (MIP) problems.

• The <Tolerance> edit box is the integer optimality or MIP-gap tolerance used in the branch and bound method.

• The GRG2 algorithm uses the <Convergence> edit box and<Estimates>, <Derivatives>, and <Search> option buttongroups.

Some Optimization Software Tools:Matlab Optimization Toolbox



26


MatLab Optimization Toolbox fmincon()


MatLab Optimization Toolbox fmincon()



27


Objective Function


Non-Linear Constraints



28


Example: Bulk Carrier Dimensioning (1)• The simplified Model used on the example is based on the

one presented in Xuebin (2009)

• This is a part of the Matlab code to call the optimizer:

% Initial pointLpp = 185.0;B = 26.0;D = 14.5;T = 10.5;Vs = 15.0;Cb = 0.70;

x0 = [Lpp B D T Vs Cb];

% Call optimizer[x, acc, exitflag, output] = fmincon( @CalcModel, x0, [], [], ...

[], [], [], [], @mycon, options );


Example: Bulk Carrier Dimensioning (2)

The file <CalcModel.m> defines the sequence of thecalculations required to compute the objective function:

function [annualCargoCost] = CalcModel( x )

% Design independent variables

Lpp = x(1);B = x(2);D = x(3);T = x(4);Vs = x(5);Cb = x(6);

displ = 1.025*Lpp*B*T*Cb;

% Froude NumberFn = 0.5144*Vs/sqrt(9.8065*Lpp);

…………

annualCargoCost = aoc/nvr/cdw;



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Example: Bulk Carrier Dimensioning (3)The file <mycon.m> contains the definition of the constraints:

function [c, ceq] = mycon( x )

global Fn dw;

Lpp = x(1);B = x(2);D = x(3);T = x(4);Vs = x(5);Cb = x(6);

% Stabilitykb = 0.53*T;bmt = (0.085*Cb - 0.002)*B*B/T/Cb;kmt = kb + bmt;gmt = kmt - (1.0 + 0.52*D);


Example: Bulk Carrier Dimensioning (4)

% Inequality Constraints defined as% ax + b <= 0

c = [-Lpp/B+6.0 Lpp/D-15.0 Lpp/T-19.0 ...

T-0.45*dw^0.31 T-0.7*D-0.7 ...25000-dw dw-500000 ...0.63-Cb Cb-0.75 ...14.0-Vs Vs-18.0 ...Lpp-274.32 Fn-0.32 ...-gmt+0.07*B];

% NO equality constraintsceq = [];

The file <mycon.m> with the definition of the constraints:



30


Example: Bulk Carrier Dimensioning (4)Final results of the optimization :

Algorithm used : medium-scale: SQP, Quasi-Newton, line-searchNo. of iterations = 18No. function calls = 133

Optimum Ship:Lpp = 221.855 mB = 36.976 mD = 19.821 mT = 14.575 mVs = 14.000 knotsCb = 0.720

ACC = 7.972 US$/t

The results are quite similar to those obtained from the Excelspreadsheet using the Solver.

Linear Programming (LP) Methods Applied toShip Dimensioning



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Introduction• Linear Programming (LP), is an Operations Research

technique that was first applied during the Second WorldWar to help solve troop-supply problems.


LP Software Tools Available

• MatLab Optimization Toolbox– Quadric and Linear Programming

• LP Solve (ANSI C)– Current version: 5.5 (CD-ROM#68)

• Clp - COIN-OR Linear Programming Solver (C++)– Current version: 1.10 (CD-ROM#68)



32

Bibliography


Bibliography (1)

9 Artana, Ketut Buda and Ishida, Kenji (2003), "The Determinationof Optimum Ship’s Design and Power Prediction Using SpreadsheetModel", Journal of the JIME Vol. 37, No. 6.

9 Artana, K.Buda and Ishida, Kenji (2003), "Spreadsheet Modeling toDetermine Optimum Ship Main Dimensions and Power Requirementsat Basic Design Stage", Marine Technology, Vol. 40, No. 1,Jan.2003, pp. 61–70. (CD-ROM#51)

9

Brinati, HL; Augusto, AO and Conti, MB (2007), “Learning Aspectsof Procedures for Ship Concept Design Based on First Principles”,International Conference on Engineering Education – ICEE’2007,Coimbra, Portugal.

9 Burgos, D. and Martins, M. (2008), "Projeto Preliminar deEmbarcações Usando Algoritmos Genéticos", SOBENA 2008.

9 Chao, Chen (2009), "The Container Shipping Network Design underChanging Demand and Freight Rates", The Eighth InternationalSymposium on Operations Research and Its Applications(ISORA’09) Zhangjiajie, China, September 20–22, pp. 245–262.(CD-ROM#68)



33


Bibliography (2)9 Chryssostomidis, Chryssostomos (1967), "Optimization Methods

Applied to Containership Design", MsC Thesis, MIT. (CD-ROM#67)

9 Cudina, Predrag (2008), "Design Procedure and MathematicalModels in the Concept Design of Tankers and Bulk Carriers",Brodogradnja, Vol.59, No.4, pp.323-339. (CD-ROM#70)

9 De, Abhijit and Kumar, Ashish (2006), "OPTI-MARINE-WARE(Optimization of Vessel's Parameters Through SpreadsheetModel)", Journal of Naval Architecture and Marine Engineering.(CD-ROM#51)

9 Dobie, Thomas (2002), “The Importance of the Human Factors in

Ship Design”9 Frank, Darko; Klanac, Alan and Bralic, Svemir (2008), "A Concept

for Concurrent Group Design of Ships", Proceedings ofCOMPIT'08, Liege, pp.450-459. (CD-ROM#68)


Bibliography (3)

9 Frank, D.; Klanac, A. and Bralic, S. (2008), "ng.zine - A New DesignSystem for Naval Architecture", Proceedings of SORTA'08, Pula.(CD-ROM#68)

9 Ganesan, Vikram (2001), "Global Optimization of the NonconvexContainership Design Problem Using the Reformulation-linearizationTechnique", MSc Thesis, Virginia Polytechnic Institute and State

University. (CD-ROM#67)9 ISSC 2003, Technical Committee IV.2 Report

• Jensen, G. (1994), “Moderne Schiffslinien”, Handbuch der Werften,Vol.XXII, Hansa, pp.93.

9 Klanac, A. and Jelovica, J. (2007), "A Concept of Omni-Optimizationfor Ship Structural Design", Advancements in Marine Structures,Guedes Soares & Das (eds), Proceedings of MARSTRUCT 2007, The1st International Conference on Marine Structures, 12-14 March2007, Glasgow, UK. p. 473-481. (Taylor & Francis: London). (CD-ROM#68)



34


Bibliography (4)9 Liang, Zheng Xuan; Yan, Lin and Shang, Ji Zhuo (2009),

"Collaborative Multidisciplinary Decision Making Based on GameTheory in Ship Preliminary Design", Journal of Marine Science andTechnology, Vol.1, pp.334–344. (CD-ROM#70)

9 Murphy, R.; Sabat, D. and Taylor, R., “Least Cost ShipCharacteristics by Computer Techniques” (CD-ROM#33)

9 Na, S-S and Karr, D. (2002), “Product-Oriented Optimal StructuralDesign of Double-Hull Oil Tankers”, Journal of Ship Production, Vol.18, No. 4, Nov. 2002, pp. 237–248. (CD-ROM#51)

9 Parsons and Scott (2004), “Formulation of Design Optimization

Problems for Solution with Scalar Numerical OptimizationMethods”, Journal of Ship Research, Vol.48, No.1, pp.61-76. (CD-ROM#51)


Bibliography (5)

9 Peri, D. and Campana, E. F. (2003), “Multidisciplinary DesignOptimization of a Naval Surface Combatant”, Journal of ShipResearch, Vol.47, No.1, pp.1-12. (CD-ROM#51)

9 Ross, J.; McNatt, T. and Hazen, G. (2002), “The Project 21 SmartProduct Model: A New Paradigm for Ship Design, Cost Estimation,and Production Planning”, Journal of Ship Production, Vol. 18, No. 2,May 2002, pp. 73–78. (CD-ROM#33)

9Schiller, T.R.; Daidola, J. C.; Kloetzli, J.C. and Pfister, J. (2001),"Portfolio of Ship Designs: Early-Stage Design Tools", MarineTechnology, Vol.38, No.2, April 2001, pp.71–91. (CD-ROM#51)

9 Schneekluth, H. e Bertram, V. (1998), “Ship Design for Efficiencyand Economy”, Butterworth-Heinemann.

• Watson, DGM and Gilfillan, AW (1976), “Some Ship DesignMethods”, RINA Transactions, Vol.119, pp.279-324.

• Whiton, Justin C. (1967), "Some Constraints On Shipping in LinearProgramming Models", Naval Research Logistics Quarterly, Vol. 14,Issue 2, pp.257-260.



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Bibliography (6)9 Xinlian, Xie; Tengfei, Wang and Daisong, Chen (2000), "A Dynamic

Model and Algorithm for Fleet Planning", Maritime Policy &Management, Vol.27, Issue 1, pp.53-63. (CD-ROM#68)

9 Xuebin, Li (2009), “Multiobjective Optimization and MultiattributeDecision Making Study of Ship’s Principal Parameters in ConceptualDesign”, Journal of Ship Research, Vol.53, No.2, pp.83-92.

9 Yang, Y-S; Park, C-K; Lee, K-H and Suh, J-C (2007), “A Study onthe Preliminary Ship Design Method Using Deterministic andProbabilistic Approach Including Hull Form”, Journal of StructuralMultidisciplinary Optimization, Vol.33, No.6, pp.529-539. (CD-ROM#65)

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Bibliography

Linear Programming

9 Ferris, Michael C.; Mangasarian, Olvi L. and Wright, Stephen J.(2007), “Linear Programming with MatLab", Society for Industrialand Applied Mathematics and the Mathematical ProgrammingSociety.

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Linear Programming Applied to Ship Design

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36

Annex A. Ships Statistical Data Gatheringand Processing


Ships Data Gathering (1)

• The common practice of single design or small series implies thatsome initial knowledge can be obtained from the analysis of theexisting ships

• To improve the efficiency of the process the information aboutexisting ships of the same type and in a similar range of cargocapacity should be structured in a small Data Base

• To improve the quality of the process, the Data Base should firstbe cleaned from:

– Incorrect data (from wrong sources or typing mistakes)

– Incomplete data (incomplete records with some missing fields)

– Repeated data (from identical ships produced in series)

• Keeping track of the ship identification (Name, IMO Number,building yard and year) and data source (journal, web site, etc.)will help to check and improve the data quality



37


Ships Data Gathering (2)• The registries of Lloyds Register and other classification

societies are good data sources

• A Spreadsheet can be used for data storage and for thestatistical analysis and graphic display of the results

• The main topics of interest are:

– Hull dimensions

– Propulsion machinery and electric generators

– Cargo capacity and equipment

– Others (ballast capacity, crew)

LwtIMO

No

MCRMain

Engine

Built

Year

Electr.Power

VsDWT DBLppName


Ships Data Gathering (3)

The measure(s) of the cargo capacity used depends of the ship type:

Ramps, liftsTotal lane length / number of cars/ number of trailers

Number of passengers

Ro/Ro

RoPax

Ferries

Number of passengersPassenger Ships

Cranes

Cell guidesTotal Number of TEUs (in holds,on deck, reefers)

Container carriers

Cargo pumps

Cranes

No. of Tanks / Holds

Volume cargo tanks/holds

Tankers

Bulk carriers

CargoEquipment

Measures of Cargo CapacityShip Type



38


Ships Data Gathering (4)• Other information also useful about ship systems, crew, etc.

(if available)

BallastPumps

Vol. DO CrewVol. FOVol.Ballast


Process the Compiled Data

• Based on the data compiled, a set of ratios can be computed

• These ratios help to characterize the ship class

• Allow the definition of the bounding limits to the variationof the design variables

• Support the estimative of values for which there is noinformation to support a computation, even if approximated

L/D %TEUdeck%TEU hold

CSR WB/DWB/T %TEUrefLWT/(L.B.D)CbT/DL/B



39


Ship Data Sources – Web Sites (1)

Data about ships built in Polish shipyards since 1992polship.cto.gda.pl

Lloyds Register Data about existing ships (paid)www.sea-web.com

DNV Registry of ships and characteristics. Free access.Search by Name or IMO Number

exchange.dnv.com

NotesData Source


Ship Data Sources – Publications (2)

Annual publication from RINA with good descriptions of themost representative ships of each year, including GeneralArrangement drawing and lightship weight information. Alsoavailable in CD-ROM.

Significant Ships

RINA Journal that contains some ship descriptionsNaval Architecture

Journal of the association of Spanish naval architects thatcontains some ship descriptions (in Spanish). Digital versionavailable only by subscription.

Ingenieria Naval

www.ingenierosnavales.com

Journal that contains some ship descriptions

Digital version available only by subscription.

Motor Ship

www.motorship.com

NotesData Source



40

Annex B. Physical Limitations to the MainDimensions of the Ship


Physical Limitations

• Physical limitations can be associated to the geographicalroute that the ship uses

• Limitations can be due to the existence of canals, straights,bridges, ports, locks systems

• The dimensions affected can be the Length, the Breadth,

the Draught and the Air Draught



41


Air Draught

• Designation given to thevertical distance measuredfrom the load waterline upto the upper extremity ofthe ship (top of the mast,chimney,..)

• Limited to 4.50 m in manyinland waterways in centralEurope due to the

existence of bridges


Some Physical Limitations in Canals

--

--

35.50

--

--

--

AirDraft

max [m]

12,00018.0055.00427.00Panama Canal(after 2014)

21.00

20.12

9.10

9.50

12.04

Tmax

[m]

300,000

240,000

65,000

DW Max.

[t]

18,000------Strait of Malacca

17,000-----Suez Canal

22.86222.50St. LawrenceCanal

40.00315.00Kiel Canal

4,00032.31294.13Panama Canal

TEU Max.Bmax

[m]

Lmax

[m]

Updated on Jan. 2010



42


Strategic Points for the Marine

Transportation


Panama Canal



43


Enlargement of the Panama CanalAdapted to Post-Panamax ships

Dimensions of the new locks (eclusas ):

L = 427 m

B = 55 m

T = 18 m

Cost: 5.5 billion US$

Beginning of work: 2007

Conclusion: 2014


Suez Canal – Navigation Chanel

Updated on Jan. 2010



45


Restrictions in Portuguese Ports

Petrol.= 23 m

Grain.= ?

Petrol.= 28 mGrain.= 17 m

Gen.Cargo= 5.5 m

Gen.Cargo = 125 mSines

Barra= 9.5 m(dep. on the tide)

250 mSetúbal

Barra= 10.5 m

(dep. on the tide)Liscont = 10 m

Sta.Apol. = 8 m

Trafaria = 12 mBarreiro = 9 m

Seixal = 5m

Trafaria = 235 mLisboa

4.7 m100 mFigueira da Foz

8 m140 m Aveiro

???Leixões/

Other ships

Station B = 9 mStation C = 5.8 m

Station B = 200 mStation C = 100 m

Leixões/

tankers

Air DraughtDraughtBreadthLength


Marine Ports in Portuguese Coast

• Viana do Castelo

• Leixões

• Aveiro

• Figueira da Foz

• Peniche

• Lisboa

• Cascais

• Sesimbra

• Setúbal

• Sines

• Lagos

• Faro

• V. R. Sto. António



46


Links – Portuguese Ports• www.portosdeportugal.pt

• www.portodeaveiro.pt

• www.portodelisboa.pt

• www.portodelisboa.pt

• www.portodesines.pt

• www.portodesetubal.pt

• www.apdl.pt


Links – Ports and Canals

• www.pancanal.com (Panama Canal)

• www.suezcanal.gov.eg (Suez Canal Authority)

• www.kiel-canal.org (Kiel Canal)

• www.greatlakes-seaway.com

• www.atlas.com.eg/scg.html• www.nnc.egnet.net/suezrules.htm

• www.portguide.com



47

Annex C. Economical Measures of Merit


Measures of Merit

• The type of measure of merit used depends on the previousknowledge of the earnings of the ship

• Known Results– Net Present Value (NPV)

– Internal Rate of Return (IRR)

• Unknown Results– Required Freight Rate (RFR)

– Present Value (PV)

– Average Annual Cost (AAC)



48


Net Present Value (NPV)• Often used when the funds for investment are limited and

the maximum income tax possible is required.

( ) ( ) ( )0

1

N

N P V P W Q F R P W A O C P W C = ⋅ − −⎡ ⎤⎣ ⎦∑

where:N - No. years of ship’s lifePW() - Present WorthQ - Total quantity of cargo carried annuallyFR - Freight TaxAOC – Annual Operating CostsC 0 - Initial ship cost


Internal Rate of Return (IRR)

• Represents the tax of return which originates equal valuesfor the Present Value of the results and of the costs, i.e.,for which NPV = 0.

• Allows more effective comparisons between entirelydifferent alternatives

• While NPV is expressed in currency units (Euro, US$), theIRR is expressed in percentage (%)

• One advantage of the IRR is that it can be computed withoutthe need to estimate the cost of the capital

• When the IRR is used, the criterion is to select the projectswhose IRR exceeds the cost of the capital



49


Required Freight Rate (RFR)• Used when the data necessary to determine accurately the

exploitation results is not available.

• Specially advantageous when comparisons are made betweenships of different sizes.

• Represents the cost per unit of cargo, necessary to coverentirely the operation costs and to guarantee the specifiedincome tax from the capital invested.

Q

C AOC RFR i+

=

where Ci is the annual cost of the capital and V R is theresidual value of the ship

( )0i RC CRF C PW V = − ⋅CRF = Capital Recovery

Factor


Permissible Price (PP)

• Represents the maximum admissible price of the ship thatstill guarantees a specified income tax.

• With the exception of the cases where the ship is paid in asingle installment, it is determined by an iterativeprocess.

• Can be used to evaluate prices in proposals of new buildingsor in the acquisition of second-hand ships, and in thecomparison of those prices with the current ship prices andfreight values.



50


Some Elementary Concepts• Capital Recovery Factor - is factor that converts a present

value into a stream of equal annual payments over a specifiedperiod of time at a given interest rate.

( )

( )

1

1 1

N

N

i iCRF

i

+=

+ −

• Present Worth Factor - is a multiplier which converts a

future amount into a present amount

( )PW iN

= +−

1

where:N No. years of ship’s lifei Interest rate


Bibliography

9 BTE (1982), “An Estimate of Operating Costs for Bulk, RoRo andContainers Ships”, Bureau of Transport Economics, Camberra.

9 Watson, D.G.M. (1998), “Practical Ship Design”, Vol.1, Elsevier.

9 Y-S Yang, C-K Park, K-H Lee and J-C Suh (2007), “A Study on thePreliminary Ship Design Method Using Deterministic Approach andProbabilistic Approach Including Hull Form”, Structural andMultidisciplinay Optimization, Vol.33, No.6, pp.529-539. (CD-ROM#50)



51


Some Relevant Links (1)

Ship search by IMO Numberwww.equasys.org

BRS Barry Rogliano Salles Shipbrokers – statisticsabout the marine transport market

www.brs-paris.com

Updated prices of the different types of Fuel Oil.

Historic record and trends.www.bunkerworld.com

Prices of second-hand shipswww.priyablue.com

Routes and distances between portswww.dataloy.com

CESA – Committee of European Shipyards’Associations

www.cesa-shipbuilding.org

AWES – Association of European Shipbuilders andShiprepairers

www.awes-shipbuilding.org

Description / NotesWeb Site


Some Relevant Links (2)

Ship search by IMO Number, Name, Owner (freeregistration)

www.shippingdatabase.com

World Shipping Register (subscription)

Search by Name, Type, DW, etc.e-ships.net

Description / NotesWeb Site




Some Portuguese Links• www.imarpor.pt (Instituto Portuário dos Transportes Marítimos)

• www.ancruzeiros.pt (Lista de Legislação Náutica de Recreio)

• www.fpvela.pt (Federação Portuguesa de Vela)

• www.hidrografico.pt (Instituto Hidrográfico)

• www.isn.org.pt (Instituto de Socorros a Náufragos)

sd 1.1.2 ship dimensioning

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