3D plotting of the bounding box around few boxes

hi,

im trying to plot a bounding box around the final arrangement of boxes but im not getting the ideal output.

this is how the final arrangement of the boxes looks:

expected output:

the output im getting:

this is the flow of my code:

1. i get the vertices of the 5 boxes in the final arrangement and parse them to a spreadsheet.

2. i use another function to plot the bounding bos from the data from spreadsheet.

i have attached all my .m files for reference. i need help on how to plot bounding box according to the boxes arrangement.

Answer 1

[Voss]

Two problems that I can see:

1. The tranpose here (actually it's a complex-conjugate tranpose):

item_vertices = vertices{i}'; % Transpose to get rows as vertices

csv_data{i+1} = [i, item_vertices(:)']; % Add item ID and flatten vertex coordinates

makes the coordinates output in the order of x1,x2,x3,x4,...,x8,y1,y2,...,y8,z1,...,z8, which doesn't match the order given in the csv header (x1,x2,x3,y1,y2,y3,...). Without the tranpose, the order matches the csv header.

2. The order of the vertices is different than in your previous question, which the bounding box plot code was written for. Therefore the connections between the vertices need to be specified differently here than before. See below for one fix; although it's probably best if you use a plotting code that makes sense to you, like your plotBins.

I marked the lines of code I modified with "CHANGED".

clc;
clear;
close all;

%% Problem Definition
model = CreateModel();  % Create Bin Packing Model
CostFunction = @(x) BinPackingCost(x, model);  % Objective Function
nVar = model.n * 4;  % Each item now has a dimension setting (x, y, z, orientation)
VarSize = [1 nVar];    % Decision Variables Matrix Size
VarMin = 0;            % Lower Bound of Decision Variables
VarMax = 1;            % Upper Bound of Decision Variables


%% PSO Parameters
MaxIt = 1000;          % Maximum Number of Iterations
nPop = 50;             % Population Size (Swarm Size)
w = 1;                 % Inertia Weight
wdamp = 0.99;          % Inertia Weight Damping Ratio
c1 = 1.5;              % Personal Learning Coefficient
c2 = 2.0;              % Global Learning Coefficient
VelMax = 0.1 * (VarMax - VarMin); % Velocity Limits
VelMin = -VelMax;

%% Initialization
particle = repmat(struct('Position', [], 'Cost', [], 'Sol', [], 'Velocity', [], 'Best', struct()), nPop, 1);
GlobalBest.Cost = inf;

for i = 1:nPop
    % Initialize position with additional orientation indices
    particle(i).Position = unifrnd(VarMin, VarMax, VarSize);
    % Ensure orientation indices are integers between 1 and 6
    particle(i).Position(4:4:end) = randi(6, 1, model.n);
    % Velocity and other initialization as before
    particle(i).Velocity = zeros(VarSize);
    [particle(i).Cost, particle(i).Sol] = CostFunction(particle(i).Position);
    particle(i).Best.Position = particle(i).Position;
    particle(i).Best.Cost = particle(i).Cost;
    particle(i).Best.Sol = particle(i).Sol;
    if particle(i).Best.Cost < GlobalBest.Cost
        GlobalBest = particle(i).Best;
    end
end

BestCost = zeros(MaxIt, 1);

%% PSO Main Loop
for it = 1:MaxIt
    for i = 1:nPop
        particle(i).Velocity = w * particle(i).Velocity ...
            + c1 * rand(VarSize) .* (particle(i).Best.Position - particle(i).Position) ...
            + c2 * rand(VarSize) .* (GlobalBest.Position - particle(i).Position);
        particle(i).Velocity = max(particle(i).Velocity, VelMin);
        particle(i).Velocity = min(particle(i).Velocity, VelMax);
        particle(i).Position = particle(i).Position + particle(i).Velocity;
        particle(i).Position = max(particle(i).Position, VarMin);
        particle(i).Position = min(particle(i).Position, VarMax);
        [particle(i).Cost, particle(i).Sol] = CostFunction(particle(i).Position);
        if particle(i).Cost < particle(i).Best.Cost
            particle(i).Best.Position = particle(i).Position;
            particle(i).Best.Cost = particle(i).Cost;
            particle(i).Best.Sol = particle(i).Sol;
            if particle(i).Best.Cost < GlobalBest.Cost
                GlobalBest = particle(i).Best;
            end
        end
    end
    BestCost(it) = GlobalBest.Cost;
    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
    w = w * wdamp;
end

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Iteration 999: Best Cost = 0
Iteration 1000: Best Cost = 0

%% Results
figure;
plot(BestCost, 'LineWidth', 2);
xlabel('Iteration');
ylabel('Best Cost');
title('Convergence of PSO');
grid on;

%% Bin Configuration Visualization and Vertex Calculation
plotBins(GlobalBest.Sol, model);

vertices = cell(model.n, 1); 

for i = GlobalBest.Sol.B{1}
    item_dims = model.items(i, :);
    orientation = GlobalBest.Position(4*i);
    item_pos = GlobalBest.Position(4*i-3:4*i-1);
    
    % Rotate dimensions based on orientation
    switch orientation
        case 1  % (L, W, H)
            rotated_dims = item_dims;
        case 2  % (L, H, W)
            rotated_dims = [item_dims(1), item_dims(3), item_dims(2)];
        case 3  % (W, L, H)
            rotated_dims = [item_dims(2), item_dims(1), item_dims(3)];
        case 4  % (W, H, L)
            rotated_dims = [item_dims(2), item_dims(3), item_dims(1)];
        case 5  % (H, L, W)
            rotated_dims = [item_dims(3), item_dims(1), item_dims(2)];
        case 6  % (H, W, L)
            rotated_dims = [item_dims(3), item_dims(2), item_dims(1)];
    end
    
    [X, Y, Z] = getBoxVertices(rotated_dims, item_pos);
    vertices{i} = [X; Y; Z]; 
end

%% Exporting Vertex Data to CSV

% Create a cell array to hold the data for the CSV file (This part remains the same)
csv_data = cell(model.n + 1, 1);
csv_data{1} = {'PresentId', 'x1', 'y1', 'z1', 'x2', 'y2', 'z2', 'x3', 'y3', 'z3', 'x4', 'y4', 'z4', 'x5', 'y5', 'z5', 'x6', 'y6', 'z6', 'x7', 'y7', 'z7', 'x8', 'y8', 'z8'};

% Fill the cell array with vertex data for each item (This part remains the same)
for i = 1:model.n
    item_vertices = vertices{i};  % CHANGED
    csv_data{i+1} = [i, item_vertices(:)']; % Add item ID and flatten vertex coordinates
end

% Open the file for writing
fileID = fopen('vertex_data.csv','w');

% Write header row (remains the same)
fprintf(fileID,'%s,',csv_data{1}{:});
fprintf(fileID,'\n');

% Write data rows (corrected loop)
for i = 2:size(csv_data,1)
    row_data = csv_data{i};  % Get the cell array for the current row
    for j = 1:length(row_data)
        fprintf(fileID,'%d,',round(row_data(j)));  % Round values to nearest integer
    end
    fprintf(fileID,'\n');
end

% Close the file (remains the same)
fclose(fileID);



%get the coordinates
solutionFile = 'vertex_data.csv';
solutionData = readmatrix(solutionFile);

% get the two bounding box corners (min(x),min(y),min(z)) and (max(x),max(y),max(z))
p1 = min(reshape(min(solutionData(:,2:end),[],1),3,[]).',[],1);
p8 = max(reshape(max(solutionData(:,2:end),[],1),3,[]).',[],1);

coordinates = solutionData(:, 2:end);

% append the bounding box coordinates to the end of the coordinates matrix:
p_bb = [p1,p1,p1,p1,p8,p8,p8,p8]; % CHANGED
p_bb([4 7 8 11]) = p8([1 1 2 2]); % CHANGED
p_bb([13 14 17 22]) = p1([1 2 2 1]); % CHANGED
coordinates(end+1,:) = p_bb;

N = size(coordinates,1);
colors = [0 1 0; 0 0 1; 1 0 0; 1 1 0; 1 0 1];
colors = repmat(colors,ceil(N/size(colors,1)),1);
% make the bounding box a special color:
colors(N,:) = [0.8 0.8 0.8];

figure

F = [1 2 3 4; 5 6 7 8; 1 2 6 5; 4 3 7 8; 2 6 7 3; 1 5 8 4]; % CHANGED

for ii = 1:N
    C = reshape(coordinates(ii, :), 3, []).';
    patch( ...
        'Vertices',C, ...
        'Faces',F, ...
        'FaceColor','flat', ...
        'FaceVertexCData',colors(ii,:), ...
        'FaceAlpha',0.5);
end

view(3)

xlabel('X-axis');
ylabel('Y-axis');
zlabel('Z-axis');
title('Visualization of Boxes');
grid on;

The boxes overlap is why they look funny but they are all single-colored cuboids.

[Thulasy Chandran]

hi, i do have a question here. does the tranpose causes the items to be arranged in a line?

just wondering how i got a different visual output which has no overlapping.

[Voss]

"does the tranpose causes the items to be arranged in a line?"

No. They are arranged like that because of how plotBins works. In particular, in plotBins, you use

getBoxVertices(item_dims, [0, 0, base_z])

% ^^^^^^^^^^^^^^

which places a lower corner of each box at x=0, y=0, z=base_z, where base_z is incremented after each box is plotted:

base_z = base_z + item_dims(3); % Increment base_z by item height

That's why they are stacked up like that.

[Thulasy Chandran]

i understand. is there a way to arrange the boxes so that minimum volume is obtained without overlapping with one and another? im trying to come up with this solution but its not working.

[Voss]

Do you mean minimum volume of the bounding box? If that's what you mean, I don't know the answer.

[Thulasy Chandran]

i mean the arrangement of the items within the boundary boxes. is there a way to prevent the tems to be placed in the same position?

[Voss]

"i mean the arrangement of the items within the boundary boxes"

So, the total volume of the boxes? That doesn't depend on their arrangement.

"is there a way to prevent the tems to be placed in the same position?"

Yes, clearly your plotBins makes the boxes not overlapping.

[Thulasy Chandran]

but now plotBins do make the final arrangment of the boxes overlapping, how do i prevent that?

[Voss]

The version of plotBins I have makes them stacked on top of each other.

[Thulasy Chandran]

how do i get boundary box around these stacked up boxes?

[Voss]

clc;
clear;
close all;

%% Problem Definition
model = CreateModel();  % Create Bin Packing Model
CostFunction = @(x) BinPackingCost(x, model);  % Objective Function
nVar = model.n * 4;  % Each item now has a dimension setting (x, y, z, orientation)
VarSize = [1 nVar];    % Decision Variables Matrix Size
VarMin = 0;            % Lower Bound of Decision Variables
VarMax = 1;            % Upper Bound of Decision Variables


%% PSO Parameters
MaxIt = 1000;          % Maximum Number of Iterations
nPop = 50;             % Population Size (Swarm Size)
w = 1;                 % Inertia Weight
wdamp = 0.99;          % Inertia Weight Damping Ratio
c1 = 1.5;              % Personal Learning Coefficient
c2 = 2.0;              % Global Learning Coefficient
VelMax = 0.1 * (VarMax - VarMin); % Velocity Limits
VelMin = -VelMax;

%% Initialization
particle = repmat(struct('Position', [], 'Cost', [], 'Sol', [], 'Velocity', [], 'Best', struct()), nPop, 1);
GlobalBest.Cost = inf;

for i = 1:nPop
    % Initialize position with additional orientation indices
    particle(i).Position = unifrnd(VarMin, VarMax, VarSize);
    % Ensure orientation indices are integers between 1 and 6
    particle(i).Position(4:4:end) = randi(6, 1, model.n);
    % Velocity and other initialization as before
    particle(i).Velocity = zeros(VarSize);
    [particle(i).Cost, particle(i).Sol] = CostFunction(particle(i).Position);
    particle(i).Best.Position = particle(i).Position;
    particle(i).Best.Cost = particle(i).Cost;
    particle(i).Best.Sol = particle(i).Sol;
    if particle(i).Best.Cost < GlobalBest.Cost
        GlobalBest = particle(i).Best;
    end
end

BestCost = zeros(MaxIt, 1);

%% PSO Main Loop
for it = 1:MaxIt
    for i = 1:nPop
        particle(i).Velocity = w * particle(i).Velocity ...
            + c1 * rand(VarSize) .* (particle(i).Best.Position - particle(i).Position) ...
            + c2 * rand(VarSize) .* (GlobalBest.Position - particle(i).Position);
        particle(i).Velocity = max(particle(i).Velocity, VelMin);
        particle(i).Velocity = min(particle(i).Velocity, VelMax);
        particle(i).Position = particle(i).Position + particle(i).Velocity;
        particle(i).Position = max(particle(i).Position, VarMin);
        particle(i).Position = min(particle(i).Position, VarMax);
        [particle(i).Cost, particle(i).Sol] = CostFunction(particle(i).Position);
        if particle(i).Cost < particle(i).Best.Cost
            particle(i).Best.Position = particle(i).Position;
            particle(i).Best.Cost = particle(i).Cost;
            particle(i).Best.Sol = particle(i).Sol;
            if particle(i).Best.Cost < GlobalBest.Cost
                GlobalBest = particle(i).Best;
            end
        end
    end
    BestCost(it) = GlobalBest.Cost;
    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
    w = w * wdamp;
end

Iteration 1: Best Cost = 0
Iteration 2: Best Cost = 0
Iteration 3: Best Cost = 0
Iteration 4: Best Cost = 0
Iteration 5: Best Cost = 0
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Iteration 399: Best Cost = 0
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Iteration 441: Best Cost = 0
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Iteration 447: Best Cost = 0
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Iteration 457: Best Cost = 0
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Iteration 501: Best Cost = 0
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Iteration 999: Best Cost = 0
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%% Results
figure;
plot(BestCost, 'LineWidth', 2);
xlabel('Iteration');
ylabel('Best Cost');
title('Convergence of PSO');
grid on;

%% Bin Configuration Visualization and Vertex Calculation
plotBins(GlobalBest.Sol, model);


%% plot bounding box
items_in_bin = GlobalBest.Sol.B{1};  % All items are in one bin
base_z = 0;  % Starting z position (height)
N = numel(items_in_bin);
X = zeros(N,8);
Y = zeros(N,8);
Z = zeros(N,8);
for i = 1:N
    item_dims = model.items(items_in_bin(i), :);  % Dimensions of the ith item
    [X(i,:), Y(i,:), Z(i,:)] = getBoxVertices(item_dims, [0, 0, base_z]);
    base_z = base_z + item_dims(3);  % Increment base_z by item height
end

X = [min(X,[],'all') max(X,[],'all')]

X = 1x2

0 15

<mw-icon class=""></mw-icon>

<mw-icon class=""></mw-icon>

Y = [min(Y,[],'all') max(Y,[],'all')]

Y = 1x2

0 20

<mw-icon class=""></mw-icon>

<mw-icon class=""></mw-icon>

Z = [min(Z,[],'all') max(Z,[],'all')]

Z = 1x2

0 99

<mw-icon class=""></mw-icon>

<mw-icon class=""></mw-icon>

fill3(X([1 2 2 1]),Y([1 1 2 2]),Z([1 1 1 1]),[0.8 0.8 0.8],'FaceAlpha',0.8)
fill3(X([1 2 2 1]),Y([1 1 2 2]),Z([2 2 2 2]),[0.8 0.8 0.8],'FaceAlpha',0.8)
fill3(X([1 2 2 1]),Y([1 1 1 1]),Z([1 1 2 2]),[0.8 0.8 0.8],'FaceAlpha',0.8)
fill3(X([1 2 2 1]),Y([2 2 2 2]),Z([1 1 2 2]),[0.8 0.8 0.8],'FaceAlpha',0.8)
fill3(X([1 1 1 1]),Y([1 1 2 2]),Z([1 2 2 1]),[0.8 0.8 0.8],'FaceAlpha',0.8)
fill3(X([2 2 2 2]),Y([1 1 2 2]),Z([1 2 2 1]),[0.8 0.8 0.8],'FaceAlpha',0.8)

[Thulasy Chandran]

how do I get the vertices of items based on this arrangement and parse them to the spreadsheet?

[Voss]

clc;
clear;
close all;

%% Problem Definition
model = CreateModel();  % Create Bin Packing Model
CostFunction = @(x) BinPackingCost(x, model);  % Objective Function
nVar = model.n * 4;  % Each item now has a dimension setting (x, y, z, orientation)
VarSize = [1 nVar];    % Decision Variables Matrix Size
VarMin = 0;            % Lower Bound of Decision Variables
VarMax = 1;            % Upper Bound of Decision Variables


%% PSO Parameters
MaxIt = 1000;          % Maximum Number of Iterations
nPop = 50;             % Population Size (Swarm Size)
w = 1;                 % Inertia Weight
wdamp = 0.99;          % Inertia Weight Damping Ratio
c1 = 1.5;              % Personal Learning Coefficient
c2 = 2.0;              % Global Learning Coefficient
VelMax = 0.1 * (VarMax - VarMin); % Velocity Limits
VelMin = -VelMax;

%% Initialization
particle = repmat(struct('Position', [], 'Cost', [], 'Sol', [], 'Velocity', [], 'Best', struct()), nPop, 1);
GlobalBest.Cost = inf;

for i = 1:nPop
    % Initialize position with additional orientation indices
    particle(i).Position = unifrnd(VarMin, VarMax, VarSize);
    % Ensure orientation indices are integers between 1 and 6
    particle(i).Position(4:4:end) = randi(6, 1, model.n);
    % Velocity and other initialization as before
    particle(i).Velocity = zeros(VarSize);
    [particle(i).Cost, particle(i).Sol] = CostFunction(particle(i).Position);
    particle(i).Best.Position = particle(i).Position;
    particle(i).Best.Cost = particle(i).Cost;
    particle(i).Best.Sol = particle(i).Sol;
    if particle(i).Best.Cost < GlobalBest.Cost
        GlobalBest = particle(i).Best;
    end
end

BestCost = zeros(MaxIt, 1);

%% PSO Main Loop
for it = 1:MaxIt
    for i = 1:nPop
        particle(i).Velocity = w * particle(i).Velocity ...
            + c1 * rand(VarSize) .* (particle(i).Best.Position - particle(i).Position) ...
            + c2 * rand(VarSize) .* (GlobalBest.Position - particle(i).Position);
        particle(i).Velocity = max(particle(i).Velocity, VelMin);
        particle(i).Velocity = min(particle(i).Velocity, VelMax);
        particle(i).Position = particle(i).Position + particle(i).Velocity;
        particle(i).Position = max(particle(i).Position, VarMin);
        particle(i).Position = min(particle(i).Position, VarMax);
        [particle(i).Cost, particle(i).Sol] = CostFunction(particle(i).Position);
        if particle(i).Cost < particle(i).Best.Cost
            particle(i).Best.Position = particle(i).Position;
            particle(i).Best.Cost = particle(i).Cost;
            particle(i).Best.Sol = particle(i).Sol;
            if particle(i).Best.Cost < GlobalBest.Cost
                GlobalBest = particle(i).Best;
            end
        end
    end
    BestCost(it) = GlobalBest.Cost;
    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
    w = w * wdamp;
end

Iteration 1: Best Cost = 0
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Iteration 604: Best Cost = 0
Iteration 605: Best Cost = 0
Iteration 606: Best Cost = 0
Iteration 607: Best Cost = 0
Iteration 608: Best Cost = 0
Iteration 609: Best Cost = 0
Iteration 610: Best Cost = 0
Iteration 611: Best Cost = 0
Iteration 612: Best Cost = 0
Iteration 613: Best Cost = 0
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Iteration 615: Best Cost = 0
Iteration 616: Best Cost = 0
Iteration 617: Best Cost = 0
Iteration 618: Best Cost = 0
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Iteration 620: Best Cost = 0
Iteration 621: Best Cost = 0
Iteration 622: Best Cost = 0
Iteration 623: Best Cost = 0
Iteration 624: Best Cost = 0
Iteration 625: Best Cost = 0
Iteration 626: Best Cost = 0
Iteration 627: Best Cost = 0
Iteration 628: Best Cost = 0
Iteration 629: Best Cost = 0
Iteration 630: Best Cost = 0
Iteration 631: Best Cost = 0
Iteration 632: Best Cost = 0
Iteration 633: Best Cost = 0
Iteration 634: Best Cost = 0
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Iteration 636: Best Cost = 0
Iteration 637: Best Cost = 0
Iteration 638: Best Cost = 0
Iteration 639: Best Cost = 0
Iteration 640: Best Cost = 0
Iteration 641: Best Cost = 0
Iteration 642: Best Cost = 0
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Iteration 647: Best Cost = 0
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Iteration 649: Best Cost = 0
Iteration 650: Best Cost = 0
Iteration 651: Best Cost = 0
Iteration 652: Best Cost = 0
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Iteration 655: Best Cost = 0
Iteration 656: Best Cost = 0
Iteration 657: Best Cost = 0
Iteration 658: Best Cost = 0
Iteration 659: Best Cost = 0
Iteration 660: Best Cost = 0
Iteration 661: Best Cost = 0
Iteration 662: Best Cost = 0
Iteration 663: Best Cost = 0
Iteration 664: Best Cost = 0
Iteration 665: Best Cost = 0
Iteration 666: Best Cost = 0
Iteration 667: Best Cost = 0
Iteration 668: Best Cost = 0
Iteration 669: Best Cost = 0
Iteration 670: Best Cost = 0
Iteration 671: Best Cost = 0
Iteration 672: Best Cost = 0
Iteration 673: Best Cost = 0
Iteration 674: Best Cost = 0
Iteration 675: Best Cost = 0
Iteration 676: Best Cost = 0
Iteration 677: Best Cost = 0
Iteration 678: Best Cost = 0
Iteration 679: Best Cost = 0
Iteration 680: Best Cost = 0
Iteration 681: Best Cost = 0
Iteration 682: Best Cost = 0
Iteration 683: Best Cost = 0
Iteration 684: Best Cost = 0
Iteration 685: Best Cost = 0
Iteration 686: Best Cost = 0
Iteration 687: Best Cost = 0
Iteration 688: Best Cost = 0
Iteration 689: Best Cost = 0
Iteration 690: Best Cost = 0
Iteration 691: Best Cost = 0
Iteration 692: Best Cost = 0
Iteration 693: Best Cost = 0
Iteration 694: Best Cost = 0
Iteration 695: Best Cost = 0
Iteration 696: Best Cost = 0
Iteration 697: Best Cost = 0
Iteration 698: Best Cost = 0
Iteration 699: Best Cost = 0
Iteration 700: Best Cost = 0
Iteration 701: Best Cost = 0
Iteration 702: Best Cost = 0
Iteration 703: Best Cost = 0
Iteration 704: Best Cost = 0
Iteration 705: Best Cost = 0
Iteration 706: Best Cost = 0
Iteration 707: Best Cost = 0
Iteration 708: Best Cost = 0
Iteration 709: Best Cost = 0
Iteration 710: Best Cost = 0
Iteration 711: Best Cost = 0
Iteration 712: Best Cost = 0
Iteration 713: Best Cost = 0
Iteration 714: Best Cost = 0
Iteration 715: Best Cost = 0
Iteration 716: Best Cost = 0
Iteration 717: Best Cost = 0
Iteration 718: Best Cost = 0
Iteration 719: Best Cost = 0
Iteration 720: Best Cost = 0
Iteration 721: Best Cost = 0
Iteration 722: Best Cost = 0
Iteration 723: Best Cost = 0
Iteration 724: Best Cost = 0
Iteration 725: Best Cost = 0
Iteration 726: Best Cost = 0
Iteration 727: Best Cost = 0
Iteration 728: Best Cost = 0
Iteration 729: Best Cost = 0
Iteration 730: Best Cost = 0
Iteration 731: Best Cost = 0
Iteration 732: Best Cost = 0
Iteration 733: Best Cost = 0
Iteration 734: Best Cost = 0
Iteration 735: Best Cost = 0
Iteration 736: Best Cost = 0
Iteration 737: Best Cost = 0
Iteration 738: Best Cost = 0
Iteration 739: Best Cost = 0
Iteration 740: Best Cost = 0
Iteration 741: Best Cost = 0
Iteration 742: Best Cost = 0
Iteration 743: Best Cost = 0
Iteration 744: Best Cost = 0
Iteration 745: Best Cost = 0
Iteration 746: Best Cost = 0
Iteration 747: Best Cost = 0
Iteration 748: Best Cost = 0
Iteration 749: Best Cost = 0
Iteration 750: Best Cost = 0
Iteration 751: Best Cost = 0
Iteration 752: Best Cost = 0
Iteration 753: Best Cost = 0
Iteration 754: Best Cost = 0
Iteration 755: Best Cost = 0
Iteration 756: Best Cost = 0
Iteration 757: Best Cost = 0
Iteration 758: Best Cost = 0
Iteration 759: Best Cost = 0
Iteration 760: Best Cost = 0
Iteration 761: Best Cost = 0
Iteration 762: Best Cost = 0
Iteration 763: Best Cost = 0
Iteration 764: Best Cost = 0
Iteration 765: Best Cost = 0
Iteration 766: Best Cost = 0
Iteration 767: Best Cost = 0
Iteration 768: Best Cost = 0
Iteration 769: Best Cost = 0
Iteration 770: Best Cost = 0
Iteration 771: Best Cost = 0
Iteration 772: Best Cost = 0
Iteration 773: Best Cost = 0
Iteration 774: Best Cost = 0
Iteration 775: Best Cost = 0
Iteration 776: Best Cost = 0
Iteration 777: Best Cost = 0
Iteration 778: Best Cost = 0
Iteration 779: Best Cost = 0
Iteration 780: Best Cost = 0
Iteration 781: Best Cost = 0
Iteration 782: Best Cost = 0
Iteration 783: Best Cost = 0
Iteration 784: Best Cost = 0
Iteration 785: Best Cost = 0
Iteration 786: Best Cost = 0
Iteration 787: Best Cost = 0
Iteration 788: Best Cost = 0
Iteration 789: Best Cost = 0
Iteration 790: Best Cost = 0
Iteration 791: Best Cost = 0
Iteration 792: Best Cost = 0
Iteration 793: Best Cost = 0
Iteration 794: Best Cost = 0
Iteration 795: Best Cost = 0
Iteration 796: Best Cost = 0
Iteration 797: Best Cost = 0
Iteration 798: Best Cost = 0
Iteration 799: Best Cost = 0
Iteration 800: Best Cost = 0
Iteration 801: Best Cost = 0
Iteration 802: Best Cost = 0
Iteration 803: Best Cost = 0
Iteration 804: Best Cost = 0
Iteration 805: Best Cost = 0
Iteration 806: Best Cost = 0
Iteration 807: Best Cost = 0
Iteration 808: Best Cost = 0
Iteration 809: Best Cost = 0
Iteration 810: Best Cost = 0
Iteration 811: Best Cost = 0
Iteration 812: Best Cost = 0
Iteration 813: Best Cost = 0
Iteration 814: Best Cost = 0
Iteration 815: Best Cost = 0
Iteration 816: Best Cost = 0
Iteration 817: Best Cost = 0
Iteration 818: Best Cost = 0
Iteration 819: Best Cost = 0
Iteration 820: Best Cost = 0
Iteration 821: Best Cost = 0
Iteration 822: Best Cost = 0
Iteration 823: Best Cost = 0
Iteration 824: Best Cost = 0
Iteration 825: Best Cost = 0
Iteration 826: Best Cost = 0
Iteration 827: Best Cost = 0
Iteration 828: Best Cost = 0
Iteration 829: Best Cost = 0
Iteration 830: Best Cost = 0
Iteration 831: Best Cost = 0
Iteration 832: Best Cost = 0
Iteration 833: Best Cost = 0
Iteration 834: Best Cost = 0
Iteration 835: Best Cost = 0
Iteration 836: Best Cost = 0
Iteration 837: Best Cost = 0
Iteration 838: Best Cost = 0
Iteration 839: Best Cost = 0
Iteration 840: Best Cost = 0
Iteration 841: Best Cost = 0
Iteration 842: Best Cost = 0
Iteration 843: Best Cost = 0
Iteration 844: Best Cost = 0
Iteration 845: Best Cost = 0
Iteration 846: Best Cost = 0
Iteration 847: Best Cost = 0
Iteration 848: Best Cost = 0
Iteration 849: Best Cost = 0
Iteration 850: Best Cost = 0
Iteration 851: Best Cost = 0
Iteration 852: Best Cost = 0
Iteration 853: Best Cost = 0
Iteration 854: Best Cost = 0
Iteration 855: Best Cost = 0
Iteration 856: Best Cost = 0
Iteration 857: Best Cost = 0
Iteration 858: Best Cost = 0
Iteration 859: Best Cost = 0
Iteration 860: Best Cost = 0
Iteration 861: Best Cost = 0
Iteration 862: Best Cost = 0
Iteration 863: Best Cost = 0
Iteration 864: Best Cost = 0
Iteration 865: Best Cost = 0
Iteration 866: Best Cost = 0
Iteration 867: Best Cost = 0
Iteration 868: Best Cost = 0
Iteration 869: Best Cost = 0
Iteration 870: Best Cost = 0
Iteration 871: Best Cost = 0
Iteration 872: Best Cost = 0
Iteration 873: Best Cost = 0
Iteration 874: Best Cost = 0
Iteration 875: Best Cost = 0
Iteration 876: Best Cost = 0
Iteration 877: Best Cost = 0
Iteration 878: Best Cost = 0
Iteration 879: Best Cost = 0
Iteration 880: Best Cost = 0
Iteration 881: Best Cost = 0
Iteration 882: Best Cost = 0
Iteration 883: Best Cost = 0
Iteration 884: Best Cost = 0
Iteration 885: Best Cost = 0
Iteration 886: Best Cost = 0
Iteration 887: Best Cost = 0
Iteration 888: Best Cost = 0
Iteration 889: Best Cost = 0
Iteration 890: Best Cost = 0
Iteration 891: Best Cost = 0
Iteration 892: Best Cost = 0
Iteration 893: Best Cost = 0
Iteration 894: Best Cost = 0
Iteration 895: Best Cost = 0
Iteration 896: Best Cost = 0
Iteration 897: Best Cost = 0
Iteration 898: Best Cost = 0
Iteration 899: Best Cost = 0
Iteration 900: Best Cost = 0
Iteration 901: Best Cost = 0
Iteration 902: Best Cost = 0
Iteration 903: Best Cost = 0
Iteration 904: Best Cost = 0
Iteration 905: Best Cost = 0
Iteration 906: Best Cost = 0
Iteration 907: Best Cost = 0
Iteration 908: Best Cost = 0
Iteration 909: Best Cost = 0
Iteration 910: Best Cost = 0
Iteration 911: Best Cost = 0
Iteration 912: Best Cost = 0
Iteration 913: Best Cost = 0
Iteration 914: Best Cost = 0
Iteration 915: Best Cost = 0
Iteration 916: Best Cost = 0
Iteration 917: Best Cost = 0
Iteration 918: Best Cost = 0
Iteration 919: Best Cost = 0
Iteration 920: Best Cost = 0
Iteration 921: Best Cost = 0
Iteration 922: Best Cost = 0
Iteration 923: Best Cost = 0
Iteration 924: Best Cost = 0
Iteration 925: Best Cost = 0
Iteration 926: Best Cost = 0
Iteration 927: Best Cost = 0
Iteration 928: Best Cost = 0
Iteration 929: Best Cost = 0
Iteration 930: Best Cost = 0
Iteration 931: Best Cost = 0
Iteration 932: Best Cost = 0
Iteration 933: Best Cost = 0
Iteration 934: Best Cost = 0
Iteration 935: Best Cost = 0
Iteration 936: Best Cost = 0
Iteration 937: Best Cost = 0
Iteration 938: Best Cost = 0
Iteration 939: Best Cost = 0
Iteration 940: Best Cost = 0
Iteration 941: Best Cost = 0
Iteration 942: Best Cost = 0
Iteration 943: Best Cost = 0
Iteration 944: Best Cost = 0
Iteration 945: Best Cost = 0
Iteration 946: Best Cost = 0
Iteration 947: Best Cost = 0
Iteration 948: Best Cost = 0
Iteration 949: Best Cost = 0
Iteration 950: Best Cost = 0
Iteration 951: Best Cost = 0
Iteration 952: Best Cost = 0
Iteration 953: Best Cost = 0
Iteration 954: Best Cost = 0
Iteration 955: Best Cost = 0
Iteration 956: Best Cost = 0
Iteration 957: Best Cost = 0
Iteration 958: Best Cost = 0
Iteration 959: Best Cost = 0
Iteration 960: Best Cost = 0
Iteration 961: Best Cost = 0
Iteration 962: Best Cost = 0
Iteration 963: Best Cost = 0
Iteration 964: Best Cost = 0
Iteration 965: Best Cost = 0
Iteration 966: Best Cost = 0
Iteration 967: Best Cost = 0
Iteration 968: Best Cost = 0
Iteration 969: Best Cost = 0
Iteration 970: Best Cost = 0
Iteration 971: Best Cost = 0
Iteration 972: Best Cost = 0
Iteration 973: Best Cost = 0
Iteration 974: Best Cost = 0
Iteration 975: Best Cost = 0
Iteration 976: Best Cost = 0
Iteration 977: Best Cost = 0
Iteration 978: Best Cost = 0
Iteration 979: Best Cost = 0
Iteration 980: Best Cost = 0
Iteration 981: Best Cost = 0
Iteration 982: Best Cost = 0
Iteration 983: Best Cost = 0
Iteration 984: Best Cost = 0
Iteration 985: Best Cost = 0
Iteration 986: Best Cost = 0
Iteration 987: Best Cost = 0
Iteration 988: Best Cost = 0
Iteration 989: Best Cost = 0
Iteration 990: Best Cost = 0
Iteration 991: Best Cost = 0
Iteration 992: Best Cost = 0
Iteration 993: Best Cost = 0
Iteration 994: Best Cost = 0
Iteration 995: Best Cost = 0
Iteration 996: Best Cost = 0
Iteration 997: Best Cost = 0
Iteration 998: Best Cost = 0
Iteration 999: Best Cost = 0
Iteration 1000: Best Cost = 0

%% Results
figure;
plot(BestCost, 'LineWidth', 2);
xlabel('Iteration');
ylabel('Best Cost');
title('Convergence of PSO');
grid on;

%% Bin Configuration Visualization and Vertex Calculation
plotBins(GlobalBest.Sol, model);

%% plot bounding box
items_in_bin = GlobalBest.Sol.B{1};  % All items are in one bin
base_z = 0;  % Starting z position (height)
N = numel(items_in_bin);
X = zeros(N,8);
Y = zeros(N,8);
Z = zeros(N,8);
for i = 1:N
    item_dims = model.items(items_in_bin(i), :);  % Dimensions of the ith item
    [X(i,:), Y(i,:), Z(i,:)] = getBoxVertices(item_dims, [0, 0, base_z]);
    base_z = base_z + item_dims(3);  % Increment base_z by item height
end

Here are the vertices:

X

X = 5x8

0 10 10 0 0 10 10 0 0 8 8 0 0 8 8 0 0 5 5 0 0 5 5 0 0 15 15 0 0 15 15 0 0 12 12 0 0 12 12 0

<mw-icon class=""></mw-icon>

<mw-icon class=""></mw-icon>

Y

Y = 5x8

0 0 15 15 0 0 15 15 0 0 12 12 0 0 12 12 0 0 10 10 0 0 10 10 0 0 20 20 0 0 20 20 0 0 18 18 0 0 18 18

<mw-icon class=""></mw-icon>

<mw-icon class=""></mw-icon>

Z

Z = 5x8

0 0 0 0 20 20 20 20 20 20 20 20 38 38 38 38 38 38 38 38 52 52 52 52 52 52 52 52 77 77 77 77 77 77 77 77 99 99 99 99

<mw-icon class=""></mw-icon>

<mw-icon class=""></mw-icon>

And here's one way to write them to file:

% each row's order is: x1,y1,z1,x2,y2,z2,...
XYZ = reshape(permute(cat(3,X,Y,Z),[1 3 2]),N,[])

XYZ = 5x24

0 0 0 10 0 0 10 15 0 0 15 0 0 0 20 10 0 20 10 15 20 0 15 20 0 0 20 8 0 20 8 12 20 0 12 20 0 0 38 8 0 38 8 12 38 0 12 38 0 0 38 5 0 38 5 10 38 0 10 38 0 0 52 5 0 52 5 10 52 0 10 52 0 0 52 15 0 52 15 20 52 0 20 52 0 0 77 15 0 77 15 20 77 0 20 77 0 0 77 12 0 77 12 18 77 0 18 77 0 0 99 12 0 99 12 18 99 0 18 99

<mw-icon class=""></mw-icon>

<mw-icon class=""></mw-icon>

 writematrix(XYZ,'output.csv')

[Thulasy Chandran]

im trying to get the visualization using the code below.

%get the coordinates
solutionFile = 'vertex_data_voss.csv';
solutionData = readmatrix(solutionFile);
% get the two bounding box corners (min(x),min(y),min(z)) and (max(x),max(y),max(z))
p1 = min(reshape(min(solutionData(:,2:end),[],1),3,[]).',[],1);
p8 = max(reshape(max(solutionData(:,2:end),[],1),3,[]).',[],1);
coordinates = solutionData(:, 2:end);
% append the bounding box coordinates to the end of the coordinates matrix:
p_bb = [p1,p1,p1,p1,p8,p8,p8,p8]; % CHANGED
p_bb([4 7 8 11]) = p8([1 1 2 2]); % CHANGED
p_bb([13 14 17 22]) = p1([1 2 2 1]); % CHANGED
coordinates(end+1,:) = p_bb;
N = size(coordinates,1);
colors = [0 1 0; 0 0 1; 1 0 0; 1 1 0; 1 0 1];
colors = repmat(colors,ceil(N/size(colors,1)),1);
% make the bounding box a special color:
colors(N,:) = [0.8 0.8 0.8];
figure
F = [1 2 3 4; 5 6 7 8; 1 2 6 5; 4 3 7 8; 2 6 7 3; 1 5 8 4]; % CHANGED
for ii = 1:N
    C = reshape(coordinates(ii, :), 3, []).';
    patch( ...
        'Vertices',C, ...
        'Faces',F, ...
        'FaceColor','flat', ...
        'FaceVertexCData',colors(ii,:), ...
        'FaceAlpha',0.5);
end
view(3)
xlabel('X-axis');
ylabel('Y-axis');
zlabel('Z-axis');
title('Visualization of Boxes');
grid on;

the visualization of the boxes doesnt look as per their dimensions,

why does this happens?

[Voss]

Because plotBins has this line:

axis equal;

%get the coordinates
solutionFile = 'vertex_data_voss.csv';
solutionData = readmatrix(solutionFile);
% get the two bounding box corners (min(x),min(y),min(z)) and (max(x),max(y),max(z))
p1 = min(reshape(min(solutionData(:,2:end),[],1),3,[]).',[],1);
p8 = max(reshape(max(solutionData(:,2:end),[],1),3,[]).',[],1);
coordinates = solutionData(:, 2:end);
% append the bounding box coordinates to the end of the coordinates matrix:
p_bb = [p1,p1,p1,p1,p8,p8,p8,p8];
p_bb([4 7 8 11]) = p8([1 1 2 2]);
p_bb([13 14 17 22]) = p1([1 2 2 1]);
coordinates(end+1,:) = p_bb;
N = size(coordinates,1);
colors = [0 1 0; 0 0 1; 1 0 0; 1 1 0; 1 0 1];
colors = repmat(colors,ceil(N/size(colors,1)),1);
% make the bounding box a special color:
colors(N,:) = [0.8 0.8 0.8];
figure
F = [1 2 3 4; 5 6 7 8; 1 2 6 5; 4 3 7 8; 2 6 7 3; 1 5 8 4];
for ii = 1:N
    C = reshape(coordinates(ii, :), 3, []).';
    patch( ...
        'Vertices',C, ...
        'Faces',F, ...
        'FaceColor','flat', ...
        'FaceVertexCData',colors(ii,:), ...
        'FaceAlpha',0.5);
end
view(3)
xlabel('X-axis');
ylabel('Y-axis');
zlabel('Z-axis');
title('Visualization of Boxes');

axis equal; % ADDED THIS LINE

grid on;

[Thulasy Chandran]

is there a way to change how each box is plotted where it gets stacked up along z-axis?

[Voss]

Yes. To change the location along the z-axis, change how base_z is calculated here:

%% plot bounding box
items_in_bin = GlobalBest.Sol.B{1};  % All items are in one bin
base_z = 0;  % Starting z position (height)
N = numel(items_in_bin);
X = zeros(N,8);
Y = zeros(N,8);
Z = zeros(N,8);
for i = 1:N
    item_dims = model.items(items_in_bin(i), :);  % Dimensions of the ith item
    [X(i,:), Y(i,:), Z(i,:)] = getBoxVertices(item_dims, [0, 0, base_z]);
    base_z = base_z + item_dims(3);  % Increment base_z by item height
end

To change the location in the x and y directions change the [0, 0, in the getBoxVertices call.

[Thulasy Chandran]

i tried to modify the code according to that but im getting output as below.

[Voss]

Since you are now incrementing in the x direction instead of the z direction, you must also change

base_x = base_x + item_dims(3);

to

base_x = base_x + item_dims(1);

% ^ increment by item size in x direction not item size in z direction

in plotBins_voss and the code that plots the bounding box. See below.

clc;
clear;
close all;

%% Problem Definition
model = CreateModel();  % Create Bin Packing Model
CostFunction = @(x) BinPackingCost(x, model);  % Objective Function
nVar = model.n * 4;  % Each item now has a dimension setting (x, y, z, orientation)
VarSize = [1 nVar];    % Decision Variables Matrix Size
VarMin = 0;            % Lower Bound of Decision Variables
VarMax = 1;            % Upper Bound of Decision Variables


%% PSO Parameters
MaxIt = 1000;          % Maximum Number of Iterations
nPop = 50;             % Population Size (Swarm Size)
w = 1;                 % Inertia Weight
wdamp = 0.99;          % Inertia Weight Damping Ratio
c1 = 1.5;              % Personal Learning Coefficient
c2 = 2.0;              % Global Learning Coefficient
VelMax = 0.1 * (VarMax - VarMin); % Velocity Limits
VelMin = -VelMax;

%% Initialization
particle = repmat(struct('Position', [], 'Cost', [], 'Sol', [], 'Velocity', [], 'Best', struct()), nPop, 1);
GlobalBest.Cost = inf;

for i = 1:nPop
    % Initialize position with additional orientation indices
    particle(i).Position = unifrnd(VarMin, VarMax, VarSize);
    % Ensure orientation indices are integers between 1 and 6
    particle(i).Position(4:4:end) = randi(6, 1, model.n);
    % Velocity and other initialization as before
    particle(i).Velocity = zeros(VarSize);
    [particle(i).Cost, particle(i).Sol] = CostFunction(particle(i).Position);
    particle(i).Best.Position = particle(i).Position;
    particle(i).Best.Cost = particle(i).Cost;
    particle(i).Best.Sol = particle(i).Sol;
    if particle(i).Best.Cost < GlobalBest.Cost
        GlobalBest = particle(i).Best;
    end
end

BestCost = zeros(MaxIt, 1);

%% PSO Main Loop
for it = 1:MaxIt
    for i = 1:nPop
        particle(i).Velocity = w * particle(i).Velocity ...
            + c1 * rand(VarSize) .* (particle(i).Best.Position - particle(i).Position) ...
            + c2 * rand(VarSize) .* (GlobalBest.Position - particle(i).Position);
        particle(i).Velocity = max(particle(i).Velocity, VelMin);
        particle(i).Velocity = min(particle(i).Velocity, VelMax);
        particle(i).Position = particle(i).Position + particle(i).Velocity;
        particle(i).Position = max(particle(i).Position, VarMin);
        particle(i).Position = min(particle(i).Position, VarMax);
        [particle(i).Cost, particle(i).Sol] = CostFunction(particle(i).Position);
        if particle(i).Cost < particle(i).Best.Cost
            particle(i).Best.Position = particle(i).Position;
            particle(i).Best.Cost = particle(i).Cost;
            particle(i).Best.Sol = particle(i).Sol;
            if particle(i).Best.Cost < GlobalBest.Cost
                GlobalBest = particle(i).Best;
            end
        end
    end
    BestCost(it) = GlobalBest.Cost;
    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
    w = w * wdamp;
end

Iteration 1: Best Cost = 0
Iteration 2: Best Cost = 0
Iteration 3: Best Cost = 0
Iteration 4: Best Cost = 0
Iteration 5: Best Cost = 0
Iteration 6: Best Cost = 0
Iteration 7: Best Cost = 0
Iteration 8: Best Cost = 0
Iteration 9: Best Cost = 0
Iteration 10: Best Cost = 0
Iteration 11: Best Cost = 0
Iteration 12: Best Cost = 0
Iteration 13: Best Cost = 0
Iteration 14: Best Cost = 0
Iteration 15: Best Cost = 0
Iteration 16: Best Cost = 0
Iteration 17: Best Cost = 0
Iteration 18: Best Cost = 0
Iteration 19: Best Cost = 0
Iteration 20: Best Cost = 0
Iteration 21: Best Cost = 0
Iteration 22: Best Cost = 0
Iteration 23: Best Cost = 0
Iteration 24: Best Cost = 0
Iteration 25: Best Cost = 0
Iteration 26: Best Cost = 0
Iteration 27: Best Cost = 0
Iteration 28: Best Cost = 0
Iteration 29: Best Cost = 0
Iteration 30: Best Cost = 0
Iteration 31: Best Cost = 0
Iteration 32: Best Cost = 0
Iteration 33: Best Cost = 0
Iteration 34: Best Cost = 0
Iteration 35: Best Cost = 0
Iteration 36: Best Cost = 0
Iteration 37: Best Cost = 0
Iteration 38: Best Cost = 0
Iteration 39: Best Cost = 0
Iteration 40: Best Cost = 0
Iteration 41: Best Cost = 0
Iteration 42: Best Cost = 0
Iteration 43: Best Cost = 0
Iteration 44: Best Cost = 0
Iteration 45: Best Cost = 0
Iteration 46: Best Cost = 0
Iteration 47: Best Cost = 0
Iteration 48: Best Cost = 0
Iteration 49: Best Cost = 0
Iteration 50: Best Cost = 0
Iteration 51: Best Cost = 0
Iteration 52: Best Cost = 0
Iteration 53: Best Cost = 0
Iteration 54: Best Cost = 0
Iteration 55: Best Cost = 0
Iteration 56: Best Cost = 0
Iteration 57: Best Cost = 0
Iteration 58: Best Cost = 0
Iteration 59: Best Cost = 0
Iteration 60: Best Cost = 0
Iteration 61: Best Cost = 0
Iteration 62: Best Cost = 0
Iteration 63: Best Cost = 0
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Iteration 856: Best Cost = 0
Iteration 857: Best Cost = 0
Iteration 858: Best Cost = 0
Iteration 859: Best Cost = 0
Iteration 860: Best Cost = 0
Iteration 861: Best Cost = 0
Iteration 862: Best Cost = 0
Iteration 863: Best Cost = 0
Iteration 864: Best Cost = 0
Iteration 865: Best Cost = 0
Iteration 866: Best Cost = 0
Iteration 867: Best Cost = 0
Iteration 868: Best Cost = 0
Iteration 869: Best Cost = 0
Iteration 870: Best Cost = 0
Iteration 871: Best Cost = 0
Iteration 872: Best Cost = 0
Iteration 873: Best Cost = 0
Iteration 874: Best Cost = 0
Iteration 875: Best Cost = 0
Iteration 876: Best Cost = 0
Iteration 877: Best Cost = 0
Iteration 878: Best Cost = 0
Iteration 879: Best Cost = 0
Iteration 880: Best Cost = 0
Iteration 881: Best Cost = 0
Iteration 882: Best Cost = 0
Iteration 883: Best Cost = 0
Iteration 884: Best Cost = 0
Iteration 885: Best Cost = 0
Iteration 886: Best Cost = 0
Iteration 887: Best Cost = 0
Iteration 888: Best Cost = 0
Iteration 889: Best Cost = 0
Iteration 890: Best Cost = 0
Iteration 891: Best Cost = 0
Iteration 892: Best Cost = 0
Iteration 893: Best Cost = 0
Iteration 894: Best Cost = 0
Iteration 895: Best Cost = 0
Iteration 896: Best Cost = 0
Iteration 897: Best Cost = 0
Iteration 898: Best Cost = 0
Iteration 899: Best Cost = 0
Iteration 900: Best Cost = 0
Iteration 901: Best Cost = 0
Iteration 902: Best Cost = 0
Iteration 903: Best Cost = 0
Iteration 904: Best Cost = 0
Iteration 905: Best Cost = 0
Iteration 906: Best Cost = 0
Iteration 907: Best Cost = 0
Iteration 908: Best Cost = 0
Iteration 909: Best Cost = 0
Iteration 910: Best Cost = 0
Iteration 911: Best Cost = 0
Iteration 912: Best Cost = 0
Iteration 913: Best Cost = 0
Iteration 914: Best Cost = 0
Iteration 915: Best Cost = 0
Iteration 916: Best Cost = 0
Iteration 917: Best Cost = 0
Iteration 918: Best Cost = 0
Iteration 919: Best Cost = 0
Iteration 920: Best Cost = 0
Iteration 921: Best Cost = 0
Iteration 922: Best Cost = 0
Iteration 923: Best Cost = 0
Iteration 924: Best Cost = 0
Iteration 925: Best Cost = 0
Iteration 926: Best Cost = 0
Iteration 927: Best Cost = 0
Iteration 928: Best Cost = 0
Iteration 929: Best Cost = 0
Iteration 930: Best Cost = 0
Iteration 931: Best Cost = 0
Iteration 932: Best Cost = 0
Iteration 933: Best Cost = 0
Iteration 934: Best Cost = 0
Iteration 935: Best Cost = 0
Iteration 936: Best Cost = 0
Iteration 937: Best Cost = 0
Iteration 938: Best Cost = 0
Iteration 939: Best Cost = 0
Iteration 940: Best Cost = 0
Iteration 941: Best Cost = 0
Iteration 942: Best Cost = 0
Iteration 943: Best Cost = 0
Iteration 944: Best Cost = 0
Iteration 945: Best Cost = 0
Iteration 946: Best Cost = 0
Iteration 947: Best Cost = 0
Iteration 948: Best Cost = 0
Iteration 949: Best Cost = 0
Iteration 950: Best Cost = 0
Iteration 951: Best Cost = 0
Iteration 952: Best Cost = 0
Iteration 953: Best Cost = 0
Iteration 954: Best Cost = 0
Iteration 955: Best Cost = 0
Iteration 956: Best Cost = 0
Iteration 957: Best Cost = 0
Iteration 958: Best Cost = 0
Iteration 959: Best Cost = 0
Iteration 960: Best Cost = 0
Iteration 961: Best Cost = 0
Iteration 962: Best Cost = 0
Iteration 963: Best Cost = 0
Iteration 964: Best Cost = 0
Iteration 965: Best Cost = 0
Iteration 966: Best Cost = 0
Iteration 967: Best Cost = 0
Iteration 968: Best Cost = 0
Iteration 969: Best Cost = 0
Iteration 970: Best Cost = 0
Iteration 971: Best Cost = 0
Iteration 972: Best Cost = 0
Iteration 973: Best Cost = 0
Iteration 974: Best Cost = 0
Iteration 975: Best Cost = 0
Iteration 976: Best Cost = 0
Iteration 977: Best Cost = 0
Iteration 978: Best Cost = 0
Iteration 979: Best Cost = 0
Iteration 980: Best Cost = 0
Iteration 981: Best Cost = 0
Iteration 982: Best Cost = 0
Iteration 983: Best Cost = 0
Iteration 984: Best Cost = 0
Iteration 985: Best Cost = 0
Iteration 986: Best Cost = 0
Iteration 987: Best Cost = 0
Iteration 988: Best Cost = 0
Iteration 989: Best Cost = 0
Iteration 990: Best Cost = 0
Iteration 991: Best Cost = 0
Iteration 992: Best Cost = 0
Iteration 993: Best Cost = 0
Iteration 994: Best Cost = 0
Iteration 995: Best Cost = 0
Iteration 996: Best Cost = 0
Iteration 997: Best Cost = 0
Iteration 998: Best Cost = 0
Iteration 999: Best Cost = 0
Iteration 1000: Best Cost = 0

%% Results
figure;
plot(BestCost, 'LineWidth', 2);
xlabel('Iteration');
ylabel('Best Cost');
title('Convergence of PSO');
grid on;

%% Bin Configuration Visualization and Vertex Calculation
plotBins_voss(GlobalBest.Sol, model);


%% plot bounding box
items_in_bin = GlobalBest.Sol.B{1};  % All items are in one bin
base_x = 0;  % Starting z position (height)
N = numel(items_in_bin);
X = zeros(N,8);
Y = zeros(N,8);
Z = zeros(N,8);
for i = 1:N
    item_dims = model.items(items_in_bin(i), :);  % Dimensions of the ith item
    [X(i,:), Y(i,:), Z(i,:)] = getBoxVertices(item_dims, [base_x, 0, 0]);
    base_x = base_x + item_dims(1);  % Increment base_z by item height
end

X = [min(X,[],'all') max(X,[],'all')];
Y = [min(Y,[],'all') max(Y,[],'all')];
Z = [min(Z,[],'all') max(Z,[],'all')];

fill3(X([1 2 2 1]),Y([1 1 2 2]),Z([1 1 1 1]),[0.8 0.8 0.8],'FaceAlpha',0.8)
fill3(X([1 2 2 1]),Y([1 1 2 2]),Z([2 2 2 2]),[0.8 0.8 0.8],'FaceAlpha',0.8)
fill3(X([1 2 2 1]),Y([1 1 1 1]),Z([1 1 2 2]),[0.8 0.8 0.8],'FaceAlpha',0.8)
fill3(X([1 2 2 1]),Y([2 2 2 2]),Z([1 1 2 2]),[0.8 0.8 0.8],'FaceAlpha',0.8)
fill3(X([1 1 1 1]),Y([1 1 2 2]),Z([1 2 2 1]),[0.8 0.8 0.8],'FaceAlpha',0.8)
fill3(X([2 2 2 2]),Y([1 1 2 2]),Z([1 2 2 1]),[0.8 0.8 0.8],'FaceAlpha',0.8)

[Thulasy Chandran]

in order toget the vertices values, do i have to change this?

for i = 1:model.n
    item_vertices = vertices{i};  % CHANGED
    csv_data{i+1} = [i, item_vertices(:)']; % Add item ID and flatten vertex coordinates
end

[Voss]

No, that's still OK.

[Thulasy Chandran]

but when i try to get the vertices using plotcube, the output is not as expected.

[Voss]

Yeah, that's always been how it is. You'll have to figure it out. I don't know what you expect it to be, only you do. So think about it, and then do it. Good luck!

[Thulasy Chandran]

yes, i figured it out. thank you so much for the help :)

[Voss]

Glad to hear it!

If you would, please "Accept" this answer. I appreciate it.

[Thulasy Chandran]

sure, just one last question. using the code below,

1. is it possible to set colour of the box according to the PresentId in the vertex_data_voss.csv file?
2. is it possible to x-axis, y-axis and z-axis to the same limit which is 100?

%get the coordinates
solutionFile = 'vertex_data_voss.csv';
solutionData = readmatrix(solutionFile);
% get the two bounding box corners (min(x),min(y),min(z)) and (max(x),max(y),max(z))
p1 = min(reshape(min(solutionData(:,2:end),[],1),3,[]).',[],1);
p8 = max(reshape(max(solutionData(:,2:end),[],1),3,[]).',[],1);
coordinates = solutionData(:, 2:end);
% append the bounding box coordinates to the end of the coordinates matrix:
p_bb = [p1,p1,p1,p1,p8,p8,p8,p8];
p_bb([4 7 8 11]) = p8([1 1 2 2]);
p_bb([13 14 17 22]) = p1([1 2 2 1]);
coordinates(end+1,:) = p_bb;
N = size(coordinates,1);
colors = [0 1 0; 0 0 1; 1 0 0; 1 1 0; 1 0 1];
colors = repmat(colors,ceil(N/size(colors,1)),1);
% make the bounding box a special color:
colors(N,:) = [0.8 0.8 0.8];
figure
F = [1 2 3 4; 5 6 7 8; 1 2 6 5; 4 3 7 8; 2 6 7 3; 1 5 8 4];
for ii = 1:N
    C = reshape(coordinates(ii, :), 3, []).';
    patch( ...
        'Vertices',C, ...
        'Faces',F, ...
        'FaceColor','flat', ...
        'FaceVertexCData',colors(ii,:), ...
        'FaceAlpha',0.5);
end
view(3)
xlabel('X-axis');
ylabel('Y-axis');
zlabel('Z-axis');
title('Visualization of Boxes');

axis equal; % ADDED THIS LINE

grid on;

[Voss]

%get the coordinates
solutionFile = 'vertex_data_voss.csv';
solutionData = readmatrix(solutionFile);

ids = solutionData(:,1);
[uids,~,id_idx] = unique(ids);
Nids = numel(uids);

% get the two bounding box corners (min(x),min(y),min(z)) and (max(x),max(y),max(z))
p1 = min(reshape(min(solutionData(:,2:end),[],1),3,[]).',[],1);
p8 = max(reshape(max(solutionData(:,2:end),[],1),3,[]).',[],1);
coordinates = solutionData(:, 2:end);

% append the bounding box coordinates to the end of the coordinates matrix:
p_bb = [p1,p1,p1,p1,p8,p8,p8,p8];
p_bb([4 7 8 11]) = p8([1 1 2 2]);
p_bb([13 14 17 22]) = p1([1 2 2 1]);
coordinates(end+1,:) = p_bb;

ids(end+1) = Nids+1;
id_idx(end+1) = Nids+1;

% random colors, with grey for the bounding box:
colors = [rand(Nids,3); 0.8 0.8 0.8];

figure
F = [1 2 3 4; 5 6 7 8; 1 2 6 5; 4 3 7 8; 2 6 7 3; 1 5 8 4];
for ii = 1:size(coordinates,1)
    C = reshape(coordinates(ii, :), 3, []).';
    patch( ...
        'Vertices',C, ...
        'Faces',F, ...
        'FaceColor','flat', ...
        'FaceVertexCData',colors(id_idx(ii),:), ...
        'FaceAlpha',0.5);
end
view(3)
xlabel('X-axis');
ylabel('Y-axis');
zlabel('Z-axis');
title('Visualization of Boxes');

axis equal;

grid on;

% limits
xlim([0 100])
ylim([0 100])
zlim([0 100])

[Thulasy Chandran]

thank you :)

[Voss]

You're welcome!