Matlab Code of Genetic Algorithm for Precedence-constrained Production Sequencing and Scheduling

function [y0 y1]= population(n)
F1=zeros(1,40); % Generate zero matrix
F2 = F1;
F3 = F1;
F=F1;
O=F1;
%==========================================================================
% Labor requirement
L=[22 25 31 31 28 36 14 25 19 23 27 13 18 35 33 19 33 30 24 24 34 32 24 15 30 30 14 28 36 31 15 25 31 15 23 25 31 17 22 21];
% Material requirement
M =[6 86 55 88 63 11 6 6 77 47 73 72 19 42 22 73 56 65 59 22 42 32 56 19 29 42 34 84 18 31 83 35 89 47 65 16 31 48 68 59];
% Cost to produce the part
C=[15 21 22 8 10 13 11 3 24 23 3 6 3 10 3 14 3 2 2 1 18 16 5 19 1 4 8 12 6 6 22 1 6 16 15 23 11 17 16 11];
% Minimum number of parts required to produce
np = 12;
% Cost of running company per hour ($k)
cc = 0.17;
% Labor available
la = 650;
% Material available
ma = 1100;
% Working capital available
wa = 400;
% Product changeover
po = 2;
%==========================================================================
for k=1:n
    tt=1;
    while tt < np
% Randomly generate chromosome 
I=round(1+39*abs(rand(1,40)));
%I=sapsep(I);
%--------------------------------------------------------------------------
% Cut the chromosome based on labor constraint
A1=zeros(1,1);
A1(1) = L(I(1));
i1=2;
while sum(A1) <= la % labor in hours limited
    if I(i1) ==I(i1-1) % no changeover time
        A1(i1)=L(I(i1));
    else
        A1(i1)=L(I(i1))+po; % changeover time involved
    end
    i1=i1+1;
end 
% The feasible chromosome based on labor        
F1(k,1:i1-2)=I(1:i1-2); % the number of part selected to produce is i1-2
%--------------------------------------------------------------------------
% Cut the chromosome based on material constraint
A2=zeros(1,1);
A2(1) = M(I(1));
i2=2;
while sum(A2) <= ma % material available
        A2(i2)=M(I(i2));
        i2=i2+1;
end 
% The feasible chromosome based on material       
F2(k,1:i2-2)=I(1:i2-2); % the number of part selected to produce is i2-2
%--------------------------------------------------------------------------
% Cut the chromosome based on working capital constraint
A3=zeros(1,1);
A3(1) = C(I(1));
i3=2;

while sum(A3) <= wa % working capital constraint
    if I(i3) ==I(i3-1) % no changeover time
        g=0;
    else
        g=po; % changeover time involved
    end
    A3(i3)=C(I(i3))+ cc*(L(I(i3))+ g); % Including producing cost and running company cost
    i3=i3+1;
end 
% The feasible chromosome based on material       
F3(k,1:i3-2)=I(1:i3-2); % the number of part selected to produce is i3-2
%--------------------------------------------------------------------------
% Final chromosome that satisfies all of above constraints is determined as follows
f(1)=i1-2;
f(2)=i2-2;
f(3)=i3-3;
p=min(f); % the position to cut the initial chromosome
%-------------------------------------------------------------------
% The following commands ensure at least 12 different parts to be selected
% p - cutting point
B=zeros(40,40);
for i=1:40;
for j=1:p
    if I(j)==i
        B(i,i)=1;
    end
end
end
tt=sum(sum(B)); % total number of different parts
end
    F(k,1:p)=I(1:p); 
    O(k,:)=I;
end
%disp('It is noted that the chromosome before cutting operation is y0 and chromosome after cutting operation is y1');
y0=O;
y1=F;
end

function y= crossover(y0,y1,t)
F=zeros(2*t,40); % Generate zero matrix
Q=zeros(2,40);
Q1=zeros(1,40);
Q2=Q1;
k=1;
%==========================================================================
% Labor requirement
L=[22 25 31 31 28 36 14 25 19 23 27 13 18 35 33 19 33 30 24 24 34 32 24 15 30 30 14 28 36 31 15 25 31 15 23 25 31 17 22 21];
% Material requirement
M =[6 86 55 88 63 11 6 6 77 47 73 72 19 42 22 73 56 65 59 22 42 32 56 19 29 42 34 84 18 31 83 35 89 47 65 16 31 48 68 59];
% Cost to produce the part
C=[15 21 22 8 10 13 11 3 24 23 3 6 3 10 3 14 3 2 2 1 18 16 5 19 1 4 8 12 6 6 22 1 6 16 15 23 11 17 16 11];
% Minimum number of parts required to produce
np = 12;
% Cost of running company per hour ($k)
cc = 0.17;
% Labor available
la = 650;
% Material available
ma = 1100;
% Working capital available
wa = 400;
% Product changeover
po = 2;
[r c]=size(y0);
w=1;
%-------------------------------------------------------------------------
for i=1:t
    tt1=1;
    tt2=1;
    d=1;
        while (tt1 < np) | (tt2 < np)
% Randomly select two parent chromosomes from initial populations (y0) for
% crossover operation
h=round(1+(r-1)*rand(1,2));
.
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function y= mutation(y0,y1,t)
F=zeros(1,40); % Generate zero matrix
I=zeros(2,40);
Q=zeros(2,40);
k=1;
%==========================================================================
% Labor requirement
L=[22 25 31 31 28 36 14 25 19 23 27 13 18 35 33 19 33 30 24 24 34 32 24 15 30 30 14 28 36 31 15 25 31 15 23 25 31 17 22 21];
% Material requirement
M =[6 86 55 88 63 11 6 6 77 47 73 72 19 42 22 73 56 65 59 22 42 32 56 19 29 42 34 84 18 31 83 35 89 47 65 16 31 48 68 59];
% Cost to produce the part
C=[15 21 22 8 10 13 11 3 24 23 3 6 3 10 3 14 3 2 2 1 18 16 5 19 1 4 8 12 6 6 22 1 6 16 15 23 11 17 16 11];
% Minimum number of parts required to produce
np = 12;
% Cost of running company per hour ($k)
cc = 0.17;
% Labor available
la = 650;
% Material available
ma = 1100;
% Working capital available
wa = 400;
% Product changeover
po = 2;
v=1;

for i=1:t
%--------------------------------------------------------------------------
% Randomly select two parent chromosomes from initial populations (y0) for
% mutation operation
[r c]=size(y0);
h=round(1+(r-1)*abs(rand(1,2)));
 if h(1)==h(2)
     h=round(1+(r-1)*abs(rand(1,2))); % to reduce probability of getting two same numbers
 end
I(1,:)=y0(h(1),:); % Selected parent 1
I(2,:)=y0(h(2),:);  % Selected parent 2
tt1=1;
tt2=1;
d=1;
while (tt1 <np)| (tt2 < np)
.
.
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function y=evaluation(W)
[u v]=size(W);
R=zeros(1);
%--------------------------------------------------------------------------
for e=1:u
    x=W(e,:);
%--------------------------------------------------------------------------
A=zeros(1,1);
B=zeros(1,1);
T=A;
D=A;
S1=zeros(1,v);
%--------------------------------------------------------------------------
% Labor requirement
L=[22 25 31 31 28 36 14 25 19 23 27 13 18 35 33 19 33 30 24 24 34 32 24 15 30 30 14 28 36 31 15 25 31 15 23 25 31 17 22 21];
% Cost to produce the part
C=[15 21 22 8 10 13 11 3 24 23 3 6 3 10 3 14 3 2 2 1 18 16 5 19 1 4 8 12 6 6 22 1 6 16 15 23 11 17 16 11];
% Cost of running company per hour ($k)
cc = 0.17;
% Price to sell the part
P=[30 44 50 20 30 20 23 9 55 45 10 14 8 22 9 22 8 7 9 8 33 40 22 45 2 9 16 23 9 10 30 9 12 20 30 31 22 25 22 27];
% Deadline of the parts
DL=[30 25 10 20 25 15 30 10 25 10 25 20 5 10 25 15 5 25 10 15 25 30 25 30 15 25 20 25 30 10 15 30 5 20 10 15 20 5 30 25];
% Product changeover
po = 2;
% Labor available
la = 650;
%--------------------------------------------------------------------------
% Determine total income (I)
for i = 1:v
    if x(i) >0
    A(1,i) = P(x(i));
    end
end
I = sum(A);
%--------------------------------------------------------------------------
% Determine total cost of producing parts (CP)
for j = 1:v
    if x(j) >0
    B(1,j) = C(x(j));
    end
end
CP = sum(B);
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function y = selection(A,B,p)
[m n]=size(A);
F=zeros(p,n);
% Determine total fitness for the population
C=sum(B);
% Determine selection probability
D=B/C;
% Determine cumulative probability 
E= cumsum(D);
% Generate a vector constaining normalised random numbers
N=sort(rand(1,p));
d1=1;
d2=1;
% Select the chromosomes for new population using roulette wheel approach 
[r1 c1]=find(B==max(B));
F(1,:)=A(max(c1),:); % select the best one first
while d2 <= p-1
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clear all
clc
close all
%--------------------------------------------------------------------------
% Number of generation required
g= 1000;
% Population size
p =100;
% Number of pairs of chromosomes required for crossover
t=30;
% Number of pairs of chromosomes required for mutation
t1=20;
%--------------------------------------------------------------------------
% Generate the zeros matrix
E=zeros(p+2*t+2*t1,40);
% Randomly generate the initial population
[y0 A0]=population(p);
for i=1:g
    % Crossover operation
    A1=crossover(y0,A0,t);
    % Mutation operation
    A2=mutation(y0,A0,t1);
    % Extended population
    E(1:p,:)=A0;
    E(p+1:p+2*t,:)=A1;
    E(p+2*t+1:p+2*t+2*t1,:)=A2;
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Sorry! This is only a half of the code.