CN105354612A - Final subdivision method in loading instrument of bulk carrier - Google Patents

Abstract

The present invention discloses a final subdivision method of a loading instrument of a bulk carrier. The method comprises the following steps: inputting initial data into the loading instrument; determining an objective function, a design variable and a constraint condition of final compartment according to floating state equations; using a differential evolutionary algorithm to resolve and optimize the objective function; using the differential evolutionary algorithm to perform operations of variating, crossing and selecting; and and traversing an individual set that meets the condition and outputting a result. In the present invention, as values of xF, TPC and MTC are not used to perform calculation, and instead floating state equilibrium equations are used to set the objective function and the constraint condition; and the differential evolutionary algorithm is used to perform calculation, thereby giving a more precise result and effectively reducing errors. According to the final subdivision method of a loading instrument of a bulk carrier disclosed by the present invention, not only can a small amount of goods be used to adjust a mean draft and a trim of a vessel, but also a large number of goods can be used to adjust the mean draft and the trim; and adjustment ranges of the mean draft and the trim are wide. According to the method disclosed by the present invention, as related data needs not to be manually inquired and manually input, the manpower can be effectively reduced, and the convenience is improved.

Description

A kind of last subdivision method in bulk freighter safe load calculator

Technical field

The present invention relates to a kind of bulk freighter safe load calculator, a kind of last subdivision method particularly in bulk freighter safe load calculator.

Background technology

In the Container Loading of bulk freighter, transport and uninstall process, ensure that the safety of boats and ships and goods is an important job.Cause bulk freighter unsafe a lot of because have, wherein prestowage is unreasonable is a major reason.For ensureing the security of bulk freighter transport, IMO (International Maritime Organization (IMO)) regulation was from 1 day July in 1998, all newly-built and existing overall lengths more than 150m bulk freighter and meet the large deck opening ship that classification society rule defines and must equip safe load calculator, this safe load calculator should be able to provide shearing and the moment of flexure data of the hull of regulation.

Along with the development of shipping, bulk freighter is tending towards maximizing, but is usually subject to the restriction of berth depth when passing in and out some harbours.Bulk freighter loads by the prestowage scheme formulated before loading usually, can reach the requirement of Mean Draught and trim in theory.But when actual load, the difference loading the real draft after goods and budget is comparatively large, and trim is not identical yet.So usually reserve a part of goods during bulk freighter loading to carry out last subdivision, the Mean Draught of adjustment boats and ships and trim.

Current first mate adopts two kinds of methods substantially when carrying out the calculating of last subdivision: " load 100t and absorb water method " in document [1-4] and " the Trimming Moment method " in document [5], use Excel calculating, as shown in Figure 1 mostly.

1, use " load 100t and absorb water method " to calculate, concrete calculation procedure is as follows:

(1) what calculating was reserved treats loading amount P _{to be installed}:

P _{to be installed}=100TPC (T _requirement-T _initially)

Wherein T _initiallyfor the Mean Draught of the current original state of boats and ships, T _requirementfor the Mean Draught of end-state required, TPC is according to T _initiallythe tonnage of water discharge change caused when " the quiet hydraulic gauge of boats and ships " of inquiry boats and ships obtains the parallel floating of boats and ships or sinking 1cm;

(2) charging capacity of last subdivision is calculated, P _firstheaded by the cabin goods weight that will fill, P _tailgoods weight for deck store will fill:

Wherein: Δ t _initiallyfor the trim of the current original state of boats and ships; Δ t _requirementfor the trim of end-state required; Δ d _{f head 100}with Δ d _{a head 100}" load 100t head and the tail and absorb water change list " of being respectively inquiry boats and ships obtains first cabin and adds the knots modification that 100t goods causes the knots modification of draft fore, tail absorbs water; Δ d _{f tail 100}with Δ d _{a tail 100}deck store adds 100t goods and causes the knots modification of draft fore, the knots modification of tail drinking water.

2, use " Trimming Moment method " to calculate, concrete calculation procedure is as follows:

(1) what calculating was reserved treats loading amount P _{to be installed}:

P _{to be installed}=100TPC (T _requirement-T _initially)

(2) charging capacity of last subdivision is asked, P _firstheaded by the cabin goods weight that will fill, P _tailgoods weight for deck store will fill:

Trim knots modification formula is calculated by following Trimming Moment:

Can system of equations be obtained:

Solve

Wherein: x _pfor the ordinate of center of gravity of goods to be installed; Δ t is trim knots modification; MTC is the Trimming Moment of every centimetre, x _ffor the ordinate of the boats and ships centre of floatation, x _{g is first}for the ordinate of boats and ships first cabin center of gravity, x _{g tail}for the ordinate of boats and ships deck store center of gravity.

But there is following shortcoming in said method:

1, " load 100t drinking water method " identical with " Trimming Moment method " Computing Principle, there is following hypothesis in it: the centre of floatation longitudinal coordinate x of boats and ships Water Plane during loading _f, every MTC MTC floating parallel with boats and ships or sinking 1cm time caused water discharge change tonnage TPC constant, so its result is approximate value, especially can produce comparatively big error when drauht changes larger.

2, use " load 100t and absorb water method " and " Trimming Moment method " to calculate, be only suitable for using a small amount of goods to adjust Mean Draught and the trim of boats and ships, the scope of Mean Draught and trim adjustment is smaller.

3, need also manually to input related data according to " load 100t head and the tail absorb water change list " of boats and ships and " the quiet hydraulic gauge of boats and ships " artificial enquiry, very inconvenient.

The list of references that the present invention relates to is as follows:

[1] Luan Famin. a kind of computing method [J] of bulk freighter " last subdivision loading ". Chinese navigation, 2013,36 (2): 135-137.

[2] Luan Famin. the fast shortcut method [J] of adjustment bulk ship trim. Chinese Water Transportation, 2007 (02), 52-53.

[3] Luan Famin, Liu Jiazhao. the quick method [J] of bulk ship dress port trim adjustment. Worldwide Shipping, 2007,30 (2): 1-2.

[4] Xie Funan. in bulk ship loading process, the simplification of last trim adjustment solves [J]. naval technology, 2009 (2), 27-28.

[5] Xing Xianghui. the research [D] of Mathematical Model of Bulk Carrier's Loading Computer and application. Dalian: navigation institute of the Maritime Affairs University Of Dalian, 2000.

Summary of the invention

For solving the problems referred to above that prior art exists, the present invention will design that a kind of error of calculation is little, accommodation is large, without the need to the last subdivision method in the bulk freighter safe load calculator of artificial enquiry.

To achieve these goals, technical scheme of the present invention is as follows: a kind of last subdivision method in bulk freighter safe load calculator, comprises the steps:

A, following data are input in safe load calculator: under the first cabin of boats and ships and deck store situation out of stock, the water discharge M that before last subdivision, boats and ships are total ₀, center of gravity longitudinal coordinate the lateral coordinates of center of gravity with the vertical coordinate of center of gravity the water discharge M that after last subdivision, boats and ships are total _last, centre of buoyancy longitudinal coordinate the lateral coordinates of centre of buoyancy with the vertical coordinate of centre of buoyancy

B, the objective function determining last subdivision according to following floading condition system of equations, design variable and constraint condition:

During boats and ships balance, determined floading condition system of equations is:

Wherein: Δ is vessel displacement, ρ is the density of water, and ▽ is the displacement of volume of boats and ships; x _g, y _g, z _gbe respectively the longitudinal coordinate of boats and ships center of gravity, lateral coordinates and vertical coordinate; x _b, y _b, z _bbe respectively the longitudinal coordinate of boats and ships centre of buoyancy, lateral coordinates and vertical coordinate; θ and be respectively trim angle and the heeling angle of boats and ships.

B1, target setting function:

Objective function

$\min f\left(P\right)=|\underset{i=1}{\overset{n}{\Σ}}{p}_i-(M_{last}-M_0)|$

Wherein: P is design variable, n is the cargo hold sum for last subdivision, p _ifor the goods weight that i-th cargo hold in the cargo hold for last subdivision loads.

B2, determine design variable:

Be P=(p by objective function determination design variable ₁, p ₂... p _n).

B3, determine constraint condition:

When balancing according to boats and ships, determined floading condition equation, each cabin allow the maximum weight loaded of loading and minimum weight loaded, obtain constraint condition as follows:

Wherein p _iMaxfor the maximum weight loaded of i-th cargo hold in cabin will be adjusted, with for before adjustment drinking water except adjusting the longitudinal coordinate of boats and ships center of gravity out of my cabin, lateral coordinates and vertical coordinate; with be respectively longitudinal coordinate, lateral coordinates and the vertical coordinate that will adjust i-th cargo hold center of gravity in cabin; with be respectively the longitudinal coordinate of the rear required boats and ships centre of buoyancy of adjustment, lateral coordinates and vertical coordinate.

C, use differential evolution algorithm solve above-mentioned objective function, optimizing:

C1, Advanced group species needed for initialization differential evolution algorithm.

C11, setting Population Size are NP, NP span is 40-60;

C12, a stochastic generation NP population at individual, i.e. design variable, each individuality each method of supporting one's family into as follows:

$p_i=p_i^{\min }+rand(0,1)\×(p_i^{\max }-p_i^{\min })$

Wherein, represent a jth individuality in t generation, t=1 here;

Rand (0,1) represents one and meet equally distributed random number between 0 to 1; the cargo hold dead weight capacity maximal value chosen when representing that initialization will adjust the weight of i-th cargo hold in cabin respectively and minimum value, for different solution vectors, its every one dimension element value is all independent generation.

C13, calculate the target function value of each individuality, wherein calculate cargo hold according to " boats and ships table of tank capacities " and to freight arbitrarily under volume the longitudinal coordinate of corresponding center of gravity with it, lateral coordinates and vertical coordinate figure.Computing method are as follows:

Target function value:

$f\left(x_j^t\right)=|\underset{i=1}{\overset{n}{\Σ}}{p}_i-(M_{last}-M_0)|$

And record meets the individuality of following condition:

$f\left(x_j^t\right)<5$ And $G\left(x_j^t\right)=0$

Wherein represent violation binding occurrence.

Calculate cargo hold according to table of tank capacities to freight arbitrarily longitudinal coordinate Xg, the lateral coordinates Yg of center of gravity corresponding with it under volume Vnet, the value of vertical coordinate Zg.The data of each cargo hold store according to Vnet incremental order, have:

Vnet _min＝Vnet ₁，Vnet _max＝Vnet _last

Vnet _min, Vnet _max, Vnet ₁, Vnet _lastrepresent the minimum value of cargo hold lade net volume, net volume maximal value, the first value of storage and the end value of storage respectively.Three kinds of situations are below divided to calculate the value of Xg and Zg:

C131, work as Vnet _min≤ Vnet≤Vnet _maxtime, namely Vnet is between the minimum value, maximal value of net volume, selects Vnet adjacent with it _k-1and Vnet _kcarry out the value that interpolation tries to achieve Xg, Yg and Zg.Concrete grammar is as follows:

$Xg=\frac{{\mathrm{Xg}}_k-{\mathrm{Xg}}_{k-1}}{{\mathrm{Vnet}}_k-{\mathrm{Vnet}}_{k-1}}(Vnet-{\mathrm{Vnet}}_{k-1})+{\mathrm{Xg}}_{k-1}$

$Yg=\frac{{\mathrm{Yg}}_k-{\mathrm{Yg}}_{k-1}}{{\mathrm{Vnet}}_k-{\mathrm{Vnet}}_{k-1}}(Vnet-{\mathrm{Vnet}}_{k-1})+{\mathrm{Yg}}_{k-1}$

$Zg=\frac{{\mathrm{Zg}}_k-{\mathrm{Zg}}_{k-1}}{{\mathrm{Vnet}}_k-{\mathrm{Vnet}}_{k-1}}(Vnet-{\mathrm{Vnet}}_{k-1})+{\mathrm{Zg}}_{k-1}$

Wherein: Vnet _k-1≤ Vnet≤Vnet _k2≤k≤last

C132, work as Vnet<Vnet _mintime, namely Vnet is less than the minimum value of net volume, selects two minimum bulking value Vnet ₁and Vnet ₂carry out the value that interpolation tries to achieve Xg, Yg and Zg.Concrete grammar is as follows:

$Xg=\frac{{\mathrm{Xg}}_2-{\mathrm{Xg}}_1}{{\mathrm{Vnet}}_2-{\mathrm{Vnet}}_1}(Vnet-{\mathrm{Vnet}}_1)+{\mathrm{Xg}}_1$

$Yg=\frac{{\mathrm{Yg}}_2-{\mathrm{Yg}}_1}{{\mathrm{Vnet}}_2-{\mathrm{Vnet}}_1}(Vnet-{\mathrm{Vnet}}_1)+{\mathrm{Yg}}_1$

$Zg=\frac{{\mathrm{Zg}}_2-{\mathrm{Zg}}_1}{{\mathrm{Vnet}}_2-{\mathrm{Vnet}}_1}(Vnet-{\mathrm{Vnet}}_1)+{\mathrm{Zg}}_1$

C133, as Vnet > Vnet _maxtime, namely Vnet is greater than net volume maximal value, selects two maximum bulking value Vnet _lastand Vnet _last-1carry out the value that interpolation tries to achieve Xg, Yg and Zg.Concrete grammar is as follows:

$Xg=\frac{{\mathrm{Xg}}_{last}-{\mathrm{Xg}}_{last-1}}{{\mathrm{Vnet}}_{last}-{\mathrm{Vnet}}_{last-1}}(Vnet-{\mathrm{Vnet}}_{last-1})+{\mathrm{Xg}}_{last-1}$

$Yg=\frac{{\mathrm{Yg}}_{last}-{\mathrm{Yg}}_{last-1}}{{\mathrm{Vnet}}_{last}-{\mathrm{Vnet}}_{last-1}}(Vnet-{\mathrm{Vnet}}_{last-1})+{\mathrm{Yg}}_{last-1}$

$Zg=\frac{{\mathrm{Zg}}_{last}-{\mathrm{Zg}}_{last-1}}{{\mathrm{Vnet}}_{last}-{\mathrm{Vnet}}_{last-1}}(Vnet-{\mathrm{Vnet}}_{last-1})+{\mathrm{Zg}}_{last-1}$

C14, calculate the violation binding occurrence of each individuality, constraint condition be rewritten into following form:

$\left\{\begin{array}{cc}{g}_r\left(x_j^t\right)\&le;0,& r=1,...,q\\ h_r\left(x_j^t\right)=0,& r=q+1,...,m\end{array}\right.$

represent inequality constrain and equality constraint respectively; Q, m-q represent the quantity of inequality constrain and the quantity of equality constraint, here q=2n, m=2n+2 respectively.

The method for expressing of the value of individual violation r constraint is as follows:

$G_r\left(x_j^t\right)=\left\{\begin{array}{cc}max\{0,g_r\left(x_j^t\right)\}& 1\&le;r\&le;q\\ max\{0,|h_r\left(x_j^t\right)|-\mathrm{\&delta;}\}& q+1\&le;r\&le;m\end{array}\right.$

δ is the allowable error of equality constraint, and δ span is 0.001-0.01.

Then individual violation binding occurrence is:

$G\left(x_j^t\right)=\underset{r=1}{\overset{m}{\&Sigma;}}{G}_r\left(x_j^t\right)$

The individuality meeting constraint in C2, calculating population accounts for total individual ratio, and namely feasible rate, represents with rate, and computing method are as follows:

The value of C3, setting zoom factor F and crossover probability factor CR:

$F=\frac{(0.9-0.4)(T-t)}{T}+0.4;$

$CR=\frac{(0.9-0.3)*t}{T}+0.3;$

Wherein T is maximum evolutionary generation, and t is current algebraically.

C4, carry out making a variation, intersect and select operation with differential evolution algorithm, and make j=1;

C41, mutation operation, carry out in the following manner:

$m_j^t=x_{r_1}^t+F(x_{r_2}^t-x_{r_3}^t)$

Here r ₁, r ₂, r ₃be the random integers do not waited with j in interval [1, NP], and meet unequal mutually between two; represent that the t produced is individual for a jth variation.

C42, interlace operation, carry out in the following manner:

$u_{j,i}^t=\left\{\begin{array}{cc}{m}_{j,i}^t& if(rand(0,1)\&le;Cr)or(i=sn)\\ x_{j,i}^t& otherwise\end{array}\right.$

Wherein represent that t is for a jth experimental subjects; Sn is random integers, meet sn ∈ [1,2 ..., n]; I represents the i-th dimension;

C43, selection operation:

The modified objective function value of experiment with computing individuality and target individual, computing method are as follows:

C431, standardization target function value:

$f_{max}^t=\max f\left(x_j^t\right),j=1,...,NP,f_{\min }^t=\min f\left(x_j^t\right),j=1,...,NP$

Wherein: represent that t is for maximum target functional value in population and minimum target functional value respectively.

When time, standardization target function value is:

$f\left(x_j^t\right)=\frac{f\left(x_j^t\right)-f_{\min }^t}{f_{max}^t-f_{\min }^t}$

$f\left(u_j^t\right)=\frac{f\left(u_j^t\right)-f_{\min }^t}{f_{max}^t-f_{\min }^t}$

When time, standardization target function value is:

$f\left(x_j^t\right)=\frac{f\left(x_j^t\right)-f\left(u_j^t\right)}{f_{max}^t-f\left(u_j^t\right)}$

$f\left(u_j^t\right)=0$

When time, standardization target function value is:

$f\left(x_j^t\right)=\frac{f\left(x_j^t\right)-f_{\min }^t}{f\left(u_k^t\right)-f_{\min }^t}$

$f\left(u_j^t\right)=1$

C432, standardization are violated binding occurrence and are:

$G_{max}^t=\max G\left(x_j^t\right),j=1,...,NP,G_{\min }^t=\min G\left(x_j^t\right),j=1,...,NP$

Wherein represent that t is for violation binding occurrence maximum in population and minimum violation binding occurrence respectively.

When time, standardization is violated binding occurrence and is:

$G\left(x_j^t\right)=\frac{G\left(x_j^t\right)-G_{\min }^t}{G_{max}^t-G_{\min }^t}$

$G\left(u_j^t\right)=\frac{G\left(u_j^t\right)-G_{\min }^t}{G_{max}^t-G_{\min }^t}$

When time, standardization is violated binding occurrence and is:

$G\left(x_j^t\right)=\frac{G\left(x_j^t\right)-G\left(u_j^t\right)}{G_{max}^t-G\left(u_j^t\right)}$

$G\left(u_j^t\right)=0$

When time, standardization is violated binding occurrence and is:

$G\left(x_j^t\right)=\frac{G\left(x_j^t\right)-G_{\min }^t}{G\left(u_j^t\right)-G_{\min }^t}$

$G\left(u_j^t\right)=1$

C433, calculating individual distance with value, method is as follows:

$d\left(x_j^t\right)=\left\{\begin{array}{cc}G\left(x_j^t\right)& if\left(rate=0\right)\\ \sqrt{f{\left(x_j^t\right)}^2+G{\left(x_j^t\right)}^2}& otherwise\end{array}\right.$

$d\left(u_j^t\right)=\left\{\begin{array}{cc}G\left(u_j^t\right)& if\left(rate=0\right)\\ \sqrt{f{\left(u_j^t\right)}^2+G{\left(u_j^t\right)}^2}& otherwise\end{array}\right.$

C434, calculating penalty term, method is as follows:

$X\left(x_j^t\right)=\left\{\begin{array}{cc}0& if(rate=0)\\ G\left(x_j^t\right)& otherwise\end{array}\right.$

$X\left(u_j^t\right)=\left\{\begin{array}{cc}0& if(rate=0)\\ G\left(u_j^t\right)& otherwise\end{array}\right.$

Penalty term with for:

$p\left(x_j^t\right)=(1-rate)*X\left(x_j^t\right)+rate*Y\left(x_j^t\right)$

$p\left(u_j^t\right)=(1-rate)*X\left(u_j^t\right)+rate*Y\left(u_j^t\right)$

C435, modified objective function value, computing method are as follows:

$F\left(x_j^t\right)=d\left(x_j^t\right)+p\left(x_j^t\right)$

$F\left(u_j^t\right)=d\left(u_j^t\right)+p\left(u_j^t\right)$

C436, system of selection are as follows:

$x_j^{t+1}=\left\{\begin{array}{cc}{u}_j^t& if(F\left(u_j^t\right)\&le;F\left(x_j^t\right))\\ x_j^t& otherwise\end{array}\right.$

Wherein represent selected individuality, replace in original seed group record qualified individuality.

$f\left(x_j^{t+1}\right)<5$ And $G\left(x_j^{t+1}\right)=0$

If C44 is j<NP+1, make j=j+1, go to step C41; Otherwise go to step C5.

C5, as t < T+1 or when not finding optimum solution, make t=t+1, go to step C2; Circulation is stopped, T=1000 here after finding optimum solution or circulation T time; Go to step D; If go on record without individuality, then cannot find feasible program, provide prompting, and return steps A;

D, travel through qualified individual collections, result is exported.

Compared with prior art, the present invention has following beneficial effect:

1, because the present invention does not use x _f, TPC and MTC value calculate, and according to floading condition balance equation group target setting function and constraint condition, and use differential evolution algorithm to calculate, comparatively precise results can be provided, effectively can reduce error.

2, the present invention not only can use a small amount of goods to adjust Mean Draught and the trim of boats and ships, can also use lot cargo to adjust, and the scope of Mean Draught and trim adjustment is wide.

3, due to " load 100t head and the tail and absorb water change list " and the also manually input of " the quiet hydraulic gauge of boats and ships " related data without the need to artificial enquiry boats and ships, effectively can reduce manpower, improve convenience.

Accompanying drawing explanation

The present invention has 3, accompanying drawing, wherein:

The schematic diagram of Fig. 1 for carrying out calculating or carry out according to " Trimming Moment method " to calculate according to " load 100t and absorb water method ".

Fig. 2 is method flow diagram of the present invention.

Fig. 3 is differential evolution algorithm process flow diagram in the present invention.

Embodiment

Below in conjunction with accompanying drawing, the present invention is described further.As Figure 2-3, the NP value in step C11 is 50 to the process flow diagram of a kind of last subdivision method in bulk freighter safe load calculator; δ value in step C14 is 0.01.

Below for 25 ton ore carriers " SHANDONGRENHE ", 1 cabin and 9 cabins are selected to carry out the example calculation of last subdivision.25 ton ore carrier " SHANDONGRENHE " principal dimensions: length between perpendiculars L _pP=319.5m, molded breadth B=57m, moldeed depth D=25m, drinking water T=18m.Full ship has 9 cargo holds, is followed successively by 1 cabin, cabin 2 from bow ... 9 cabins.

Carry condition one: Mean Draught variation delta T=1.1m is larger, as shown in table 1-4

The distribution of goods amount using classic method to calculate 1 cabin and 9 cabins is respectively 8032.734t and 7393.818t, to obtain Mean Draught in this result input Onboard-Napa Load Computer and trim is respectively 3.86m and-4.07m, the error calculating Mean Draught and trim is respectively 1.026% and 8.33%;

The distribution of goods amount using the present invention to calculate 1 cabin and 9 cabins is respectively 7773.98t and 7967.01t, to obtain Mean Draught in this result input Onboard-Napa Load Computer and trim is respectively 3.90m and-4.44m, the error calculating Mean Draught and trim is respectively 0% and 0%.

Carry condition two: Mean Draught variation delta T=0.23m is smaller, as shown in table 5-8:

The distribution of goods amount using classic method to calculate 1 cabin and 9 cabins is respectively 2032.02t and 2064.27t, to obtain Mean Draught in this result input Onboard-Napa Load Computer and trim is respectively 18.64m and-0.45m, the error calculating Mean Draught and trim is respectively 0% and 2.17%;

The distribution of goods amount using the present invention to calculate 1 cabin and 9 cabins is respectively 1964.2t and 2025t, to obtain Mean Draught in this result input Onboard-Napa Load Computer and trim is respectively 18.64m and-0.46m, the error calculating Mean Draught and trim is respectively 0% and 0%.

It is less that classic method changes little time error at Mean Draught, and change greatly time error at Mean Draught and will become large; And the present invention not only to change little time error at Mean Draught less, and it is also less to change greatly time error at Mean Draught.The present invention is not only applicable to using a small amount of goods to carry out last subdivision, is also applicable to using lot cargo to carry out last subdivision, can effectively reduces error, have versatility.Subdivision result of the present invention is more reasonable, and boats and ships Mean Draught and trim can be made to reach desired value.

Carry condition one: the variation delta T=1.1m of Mean Draught is larger, RHO=1t/m ³

The forward and backward boats and ships original state of table 1 goods subdivision and expection end-state

	Mean Draught (m)	Trim (m)	Water discharge (t)	Center of gravity/centre of buoyancy
					Boats and ships original state	2.8	-4.48	34176	Center of gravity (148.77,0,14.6)
Expection end-state	3.90	-4.44	49917	Centre of buoyancy (156.02,0,2.07)

The loading amount in each cabin of boats and ships before table 2 goods subdivision

1 cabin

2 cabins

3 cabins

4 cabins

5 cabins

6 cabins

7 cabins

8 cabins

9 cabins

0t

0t

0t

0t

0t

0t

0t

0t

0t

The result of calculation of table 3 classic method and last subdivision of the present invention

	1 cabin	2 cabins	3 cabins	4 cabins	5 cabins	6 cabins	7 cabins	8 cabins	9 cabins
										Classic method	8032.734t	0	0	0	0	0	0	0	7393.818t
The present invention	7773.98t	0	0	0	0	0	0	0	7967.01t

Table 4 classic method and Comparative result of the present invention

	Mean Draught (m)	Mean Draught error (%)	Trim (m)	Trim error (%)
					Classic method	3.86	1.026	-4.07	8.33
The present invention	3.90m	0	-4.44m	0

Carry condition two: the variation delta T=0.23m of Mean Draught is smaller, RHO=1.5839t/m ³

The forward and backward Vessel's Description of table 5 goods subdivision

	Mean Draught (m)	Trim (m)	Water discharge (t)	Center of gravity/centre of buoyancy
					Boats and ships original state	18.41	-0.62	291346	Center of gravity (166.72 ,-0.01,15.39)
Expection end-state	18.64	-0.46	295336	Centre of buoyancy (166.784,0.013,9.833)

The loading amount in each cabin of boats and ships before table 6 goods subdivision

1 cabin

2 cabins

3 cabins

4 cabins

5 cabins

6 cabins

7 cabins

8 cabins

9 cabins

22174.8t

29359.5t

29038.3t

29044.3t

29044.4t

29044.4t

29044.4t

29044.3t

22703.5t

The result of calculation of table 7 classic method and last subdivision of the present invention

	1 cabin	2 cabins	3 cabins	4 cabins	5 cabins	6 cabins	7 cabins	8 cabins	9 cabins
										Classic method	2032.02t	0	0	0	0	0	0	0	2064.27t
The present invention	1964.2t	0	0	0	0	0	0	0	2025t

Table 8 classic method and Comparative result of the present invention

	Mean Draught (m)	Mean Draught error (%)	Trim (m)	Trim error (%)
					Classic method	18.64	0	-0.45	2.17
The present invention	18.64	0	-0.46	0

Claims (1)

1. the last subdivision method in bulk freighter safe load calculator, is characterized in that: comprise the steps:

A, following data are input in safe load calculator: under the first cabin of boats and ships and deck store situation out of stock, the water discharge M that before last subdivision, boats and ships are total ₀, center of gravity longitudinal coordinate the lateral coordinates of center of gravity with the vertical coordinate of center of gravity the water discharge M that after last subdivision, boats and ships are total _last, centre of buoyancy longitudinal coordinate the lateral coordinates of centre of buoyancy with the vertical coordinate of centre of buoyancy

B, the objective function determining last subdivision according to following floading condition system of equations, design variable and constraint condition:

During boats and ships balance, determined floading condition system of equations is:

Wherein: Δ is vessel displacement, ρ is the density of water, for the displacement of volume of boats and ships; x _g, y _g, z _gbe respectively the longitudinal coordinate of boats and ships center of gravity, lateral coordinates and vertical coordinate; x _b, y _b, z _bbe respectively the longitudinal coordinate of boats and ships centre of buoyancy, lateral coordinates and vertical coordinate; θ and be respectively trim angle and the heeling angle of boats and ships;

B1, target setting function:

Objective function

$\min f\left(P\right)=|\underset{i=1}{\overset{n}{\&Sigma;}}{p}_i-(M_{last}-M_0)|$

Wherein: P is design variable, n is the cargo hold sum for last subdivision, p _ifor the goods weight that i-th cargo hold in the cargo hold for last subdivision loads;

B2, determine design variable:

Be P=(p by objective function determination design variable ₁, p ₂... p _n);

B3, determine constraint condition:

When balancing according to boats and ships, determined floading condition equation, each cabin allow the maximum weight loaded of loading and minimum weight loaded, obtain constraint condition as follows:

Wherein p _iMaxfor the maximum weight loaded of i-th cargo hold in cabin will be adjusted, with for before adjustment drinking water except adjusting the longitudinal coordinate of boats and ships center of gravity out of my cabin, lateral coordinates and vertical coordinate; with be respectively longitudinal coordinate, lateral coordinates and the vertical coordinate that will adjust i-th cargo hold center of gravity in cabin; with be respectively the longitudinal coordinate of the rear required boats and ships centre of buoyancy of adjustment, lateral coordinates and vertical coordinate;

C, use differential evolution algorithm solve above-mentioned objective function, optimizing:

C1, Advanced group species needed for initialization differential evolution algorithm;

C11, setting Population Size are NP, NP span is 40-60;

C12, a stochastic generation NP population at individual, i.e. design variable, each individuality each method of supporting one's family into as follows:

$p_i=p_i^{\min }+rand(0,1)\&times;\left(p_i^{\max }-p_i^{\min }\right)$

Wherein, represent a jth individuality in t generation, t=1 here;

Rand (0,1) represents one and meet equally distributed random number between 0 to 1; the cargo hold dead weight capacity maximal value chosen when representing that initialization will adjust the weight of i-th cargo hold in cabin respectively and minimum value, for different solution vectors, its every one dimension element value is all independent generation;

C13, calculate the target function value of each individuality, wherein calculate cargo hold according to " boats and ships table of tank capacities " and to freight arbitrarily under volume the longitudinal coordinate of corresponding center of gravity with it, lateral coordinates and vertical coordinate figure; Computing method are as follows:

Target function value:

$f\left(x_j^t\right)=|\underset{i=1}{\overset{n}{\&Sigma;}}{p}_i-\left(M_{last}-M_0\right)|$

And record meets the individuality of following condition:

$f\left(x_j^t\right)<5$ And $G\left(x_j^t\right)=0$

Wherein represent violation binding occurrence;

Calculate cargo hold according to table of tank capacities to freight arbitrarily longitudinal coordinate Xg, the lateral coordinates Yg of center of gravity corresponding with it under volume Vnet, the value of vertical coordinate Zg; The data of each cargo hold store according to Vnet incremental order, have:

Vnet _min＝Vnet ₁，Vnet _max＝Vnet _last

Vnet _min, Vnet _max, Vnet ₁, Vnet _lastrepresent the minimum value of cargo hold lade net volume, net volume maximal value, the first value of storage and the end value of storage respectively; Three kinds of situations are below divided to calculate the value of Xg and Zg:

C131, work as Vnet _min≤ Vnet≤Vnet _maxtime, namely Vnet is between the minimum value, maximal value of net volume, selects Vnet adjacent with it _k-1and Vnet _kcarry out the value that interpolation tries to achieve Xg, Yg and Zg; Concrete grammar is as follows:

$Xg=\frac{{\mathrm{Xg}}_k-{\mathrm{Xg}}_{k-1}}{{\mathrm{Vnet}}_k-{\mathrm{Vnet}}_{k-1}}(Vnet-{\mathrm{Vnet}}_{k-1})+{\mathrm{Xg}}_{k-1}$

$Yg=\frac{{\mathrm{Yg}}_k-{\mathrm{Yg}}_{k-1}}{{\mathrm{Vnet}}_k-{\mathrm{Vnet}}_{k-1}}(Vnet-{\mathrm{Vnet}}_{k-1})+{\mathrm{Yg}}_{k-1}$

$Zg=\frac{{\mathrm{Zg}}_k-{\mathrm{Zg}}_{k-1}}{{\mathrm{Vnet}}_k-{\mathrm{Vnet}}_{k-1}}(Vnet-{\mathrm{Vnet}}_{k-1})+{\mathrm{Zg}}_{k-1}$

Wherein: Vnet _k-1≤ Vnet≤Vnet _k2≤k≤last

C132, work as Vnet<Vnet _mintime, namely Vnet is less than the minimum value of net volume, selects two minimum bulking value Vnet ₁and Vnet ₂carry out the value that interpolation tries to achieve Xg, Yg and Zg; Concrete grammar is as follows:

$Xg=\frac{{\mathrm{Xg}}_2-{\mathrm{Xg}}_1}{{\mathrm{Vnet}}_2-{\mathrm{Vnet}}_1}(Vnet-{\mathrm{Vnet}}_1)+{\mathrm{Xg}}_1$

$Yg=\frac{{\mathrm{Yg}}_2-{\mathrm{Yg}}_1}{{\mathrm{Vnet}}_2-{\mathrm{Vnet}}_1}(Vnet-{\mathrm{Vnet}}_1)+{\mathrm{Yg}}_1$

$Zg=\frac{{\mathrm{Zg}}_2-{\mathrm{Zg}}_1}{{\mathrm{Vnet}}_2-{\mathrm{Vnet}}_1}(Vnet-{\mathrm{Vnet}}_1)+{\mathrm{Zg}}_1$

C133, as Vnet > Vnet _maxtime, namely Vnet is greater than net volume maximal value, selects two maximum bulking value Vnet _lastand Vnet _last-1carry out the value that interpolation tries to achieve Xg, Yg and Zg; Concrete grammar is as follows:

$Xg=\frac{{\mathrm{Xg}}_{last}-{\mathrm{Xg}}_{last-1}}{{\mathrm{Vnet}}_{last}-{\mathrm{Vnet}}_{last-1}}(Vnet-{\mathrm{Vnet}}_{last-1})+{\mathrm{Xg}}_{last-1}$

$Yg=\frac{{\mathrm{Yg}}_{last}-{\mathrm{Yg}}_{last-1}}{{\mathrm{Vnet}}_{last}-{\mathrm{Vnet}}_{last-1}}(Vnet-{\mathrm{Vnet}}_{last-1})+{\mathrm{Yg}}_{last-1}$

$Zg=\frac{{\mathrm{Zg}}_{last}-{\mathrm{Zg}}_{last-1}}{{\mathrm{Vnet}}_{last}-{\mathrm{Vnet}}_{last-1}}(Vnet-{\mathrm{Vnet}}_{last-1})+{\mathrm{Zg}}_{last-1}$

C14, calculate the violation binding occurrence of each individuality, constraint condition be rewritten into following form:

$\left\{\begin{array}{cc}{g}_r\left(x_j^t\right)\&le;0,& r=1,...,q\\ h_r\left(x_j^t\right)=0,& r=q+1,...,m\end{array}\right.$

represent inequality constrain and equality constraint respectively; Q, m-q represent the quantity of inequality constrain and the quantity of equality constraint, here q=2n, m=2n+2 respectively;

The method for expressing of the value of individual violation r constraint is as follows:

$G_r\left(x_j^t\right)=\left\{\begin{array}{cc}max\{0,g_r\left(x_j^t\right)\}& 1\&le;r\&le;q\\ \max \{0,|h_r\left(x_j^t\right)|-\mathrm{\&delta;}\}& q+1\&le;r\&le;m\end{array}\right.$

δ is the allowable error of equality constraint, and δ span is 0.001-0.01;

Then individual violation binding occurrence is:

$G\left(x_j^t\right)=\underset{r=1}{\overset{m}{\&Sigma;}}{G}_r\left(x_j^t\right)$

The individuality meeting constraint in C2, calculating population accounts for total individual ratio, and namely feasible rate, represents with rate, and computing method are as follows:

The value of C3, setting zoom factor F and crossover probability factor CR:

$F=\frac{(0.9-0.4)(T-t)}{T}+0.4;$

$CR=\frac{(0.9-0.3)*t}{T}+0.3;$

Wherein T is maximum evolutionary generation, and t is current algebraically;

C4, carry out making a variation, intersect and select operation with differential evolution algorithm, and make j=1;

C41, mutation operation, carry out in the following manner:

$m_j^t=x_{r_1}^t+F(x_{r_2}^t-x_{r_3}^t)$

Here r ₁, r ₂, r ₃be the random integers do not waited with j in interval [1, NP], and meet unequal mutually between two; represent that the t produced is individual for a jth variation;

C42, interlace operation, carry out in the following manner:

$u_{j,i}^t=\left\{\begin{array}{cc}{m}_{j,i}^t& if\left(rand\right(0,1)\&le;Cr)or(i=sn)\\ x_{j,i}^t& otherwise\end{array}\right.$

Wherein represent that t is for a jth experimental subjects; Sn is random integers, meet sn ∈ [1,2 ..., n]; I represents the i-th dimension;

C43, selection operation:

The modified objective function value of experiment with computing individuality and target individual, computing method are as follows:

C431, standardization target function value:

$f_{max}^t=\max f\left(x_j^t\right),j=1,...,NP,$ $f_{\min }^t=minf\left(x_j^t\right),j=1,...,NP$

Wherein: represent that t is for maximum target functional value in population and minimum target functional value respectively;

When time, standardization target function value is:

$f\left(x_j^t\right)=\frac{f\left(x_j^t\right)-f_{\min }^t}{f_{\max }^t-f_{\min }^t}$

$f\left(u_j^t\right)=\frac{f\left(u_j^t\right)-f_{\min }^t}{f_{max}^t-f_{min}^t}$

When time, standardization target function value is:

$f\left(x_j^t\right)=\frac{f\left(x_j^t\right)-f\left(u_j^t\right)}{f_{max}^t-f\left(u_j^t\right)}$

$f\left(u_j^t\right)=0$

When time, standardization target function value is:

$f\left(x_j^t\right)=\frac{f\left(x_j^t\right)-f_{\min }^t}{f\left(u_j^t\right)-f_{min}^t}$

$f\left(u_j^t\right)=1$

C432, standardization are violated binding occurrence and are:

$G_{max}^t=\max G\left(x_j^t\right),j=1,...,NP,$ $G_{\min }^t=minG\left(x_j^t\right),j=1,...,NP$

Wherein represent that t is for violation binding occurrence maximum in population and minimum violation binding occurrence respectively;

When time, standardization is violated binding occurrence and is:

$G\left(x_j^t\right)=\frac{G\left(x_j^t\right)-G_{\min }^t}{G_{max}^t-G_{\min }^t}$

$G\left(u_j^t\right)=\frac{G\left(u_j^t\right)-G_{\min }^t}{G_{max}^t-G_{min}^t}$

When time, standardization is violated binding occurrence and is:

$G\left(x_j^t\right)=\frac{G\left(x_j^t\right)-G\left(u_j^t\right)}{G_{max}^t-G\left(u_j^t\right)}$

$G\left(u_j^t\right)=0$

When time, standardization is violated binding occurrence and is:

$G\left(x_j^t\right)=\frac{G\left(x_j^t\right)-G_{\min }^t}{G\left(u_j^t\right)-G_{\min }^t}$

$G\left(u_j^t\right)=1$

C433, calculating individual distance with value, method is as follows:

$d\left(x_j^t\right)=\left\{\begin{array}{cc}G\left(x_j^t\right)& if\left(rate=0\right)\\ \sqrt{f{\left(x_j^t\right)}^2+G{\left(x_j^t\right)}^2}& otherwise\end{array}\right.$

$d\left(u_j^t\right)=\left\{\begin{array}{cc}G\left(u_j^t\right)& if\left(rate=0\right)\\ \sqrt{f{\left(u_j^t\right)}^2+G{\left(u_j^t\right)}^2}& otherwise\end{array}\right.$

C434, calculating penalty term, method is as follows:

$X\left(x_j^t\right)=\left\{\begin{array}{cc}0& if\left(rate=0\right)\\ G\left(x_j^t\right)& otherwise\end{array}\right.$

$X\left(u_j^t\right)=\left\{\begin{array}{cc}0& if\left(rate=0\right)\\ G\left(u_j^t\right)& otherwise\end{array}\right.$

Penalty term with for:

$p\left(x_j^t\right)=(1-rate)*X\left(x_j^t\right)+rate*Y\left(x_j^t\right)$

$p\left(u_j^t\right)=(1-rate)*X\left(u_j^t\right)+rate*Y\left(u_j^t\right)$

C435, modified objective function value, computing method are as follows:

$F\left(x_j^t\right)=d\left(x_j^t\right)+p\left(x_j^t\right)$

$F\left(u_j^t\right)=d\left(u_j^t\right)+p\left(u_j^t\right)$

C436, system of selection are as follows:

$x_j^{t+1}=\left\{\begin{array}{cc}{u}_j^t& if\left(F\right(u_j^t)\&le;F(x_j^t\left)\right)\\ x_j^t& otherwise\end{array}\right.$

Wherein represent selected individuality, replace in original seed group record qualified individuality;

$f\left(x_j^{t+1}\right)<5$ And $G\left(x_j^{t+1}\right)=0$

If C44 is j<NP+1, make j=j+1, go to step C41; Otherwise go to step C5;

C5, as t < T+1 or when not finding optimum solution, make t=t+1, go to step C2; Circulation is stopped, T=1000 here after finding optimum solution or circulation T time; Go to step D; If go on record without individuality, then cannot find feasible program, provide prompting, and return steps A;

D, travel through qualified individual collections, result is exported.