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clear;clc;close all;
%% Parameters
p=5;
s=5;
t=10;
for lambda=0.2:0.2:0.2
for n=1:5
pc=80+10*ones(p,s,t);%reshape(xlsread('data.xlsx','sheet1','A2:A33'),p,s,t);
pc1=120+10*ones(p,s,t);
oc=200+150*ones(p,s,t);%reshape(xlsread('data.xlsx','sheet1','B2:B33'),p,s,t);
hc=5+5*ones(p,t);%reshape(xlsread('data.xlsx','sheet1','G2:G17'),p,t);
tc=10+8*ones(s,t);%xlsread('data.xlsx','sheet1','D2:D5');
D0=500+100*ones(p,t);%reshape(xlsread('data.xlsx','sheet1','H2:H17'),p,t);
sc=800+400*ones(p,s,t);%reshape(xlsread('data.xlsx','sheet1','C2:C33'),p,s,t);
eo=2+ones(p,s,t);%reshape(xlsread('data.xlsx','sheet1','P2:P33'),p,s,t);
eh=1+2*ones(p,t);%reshape(xlsread('data.xlsx','sheet1','N2:N17'),p,t);
ev=2+3*ones(p,s,t);%reshape(xlsread('data.xlsx','sheet1','I2:I33'),p,s,t);
w=350+200*ones(1,t);%xlsread('data.xlsx','sheet1','O2:O5');
dis=100+50*ones(1,s);%[120 150];
cc=4000;
r=[0.01,0.1,0.1,0.05,0.15];
cp=50;%60+10*rand;
b=2+0.3*rand; gam=2;
eta1=500000; epsilon=0.9; eta=0.1;theta=0.1;%lambda=0.9;
theta1=150;p0=[0.3;0.6;0.1];e0=[1;2;3];e=e0/norm(e0,1);
%p0=xlsread('data.xlsx','box','ah2:ah81');e0=xlsread('data.xlsx','box','z2:z81');e=e0/norm(e0,1);
m=3;
x=sdpvar(p,s,t);
y=binvar(p,s,t);
x1=sdpvar(p,s,t);
y1=binvar(p,s,t);
z=sdpvar(p,t);
sh=sdpvar(p,t);
u=sdpvar(1);beta=sdpvar(1);beta0=sdpvar(1);tao=sdpvar(m,1);tao0=sdpvar(m,1);
r1=sdpvar(1);r2=sdpvar(m,1);r3=sdpvar(m,1);r4=sdpvar(m,1);
lambda1=sdpvar(1);lambda2=sdpvar(m,1);lambda3=sdpvar(m,1);lambda4=sdpvar(m,1);
r10=sdpvar(1);r20=sdpvar(m,1);r30=sdpvar(m,1);r40=sdpvar(m,1);
lambda10=sdpvar(1);lambda20=sdpvar(m,1);lambda30=sdpvar(m,1);lambda40=sdpvar(m,1);
%% Objective function
obj=lambda*beta+(lambda/(1-epsilon))*(tao'*p0+(lambda2'+lambda3')*e*gam)+(1-lambda)*(sum(sum(sum(eta1.*D0)))*ones(1,m)*p0...
+(r2'+r3')*e*gam)+10*sum(squeeze(sum(sum((x+x1),1).*tc,3)).*dis) + sum(sum(sum(oc.*(y+y1))))+sum(sum(hc.*z))+u*cp...
+sum(theta1.*r.*(sum(sum(y+y1,1),3)))+sum(sum(sum((x1).*pc1)))+sum(sum(eta1.*(-sum(x,2)-sum(x1,2))))+sum(z(:,10))...
+sum(sum(sum((x1).*pc1)))+sum(sum(sum((x).*pc)));%
%% Constraint
C=[x(:)>=0];
C=[C,x1(:)>=0];
C=[C,z(:)>=0];
C=[C,sh>=0];
%% disruption
for i=1:2
y(:,i,:)=0;
end
C=[C,r2(:)>=0];C=[C,r3>=0];C=[C,r4(:)>=0];
C=[C,lambda2(:)>=0];C=[C,lambda3>=0];C=[C,lambda4(:)>=0];
C=[C,r20(:)>=0];C=[C,r30>=0];C=[C,r40(:)>=0];
C=[C,lambda20(:)>=0];C=[C,lambda30>=0];C=[C,lambda40(:)>=0];
C=[C,tao(:)>=0];C=[C,tao0(:)>=0];
C=[C,x<=sc.*y];
C=[C,x1<=(sc-x).*y1];
C =[C,sum(z,1)<=w];
for i=1:p
for j=1:t
C =[C,sum(r.*y1(i,:,j),1)<=0.2];
end
end
C =[C,-squeeze(sum(x(:,:,1),2))-squeeze(sum(x1(:,:,1),2))+D0(:,1)+z(:,1)>=0];
for i=2:t
C =[C,-z(:,i-1) - squeeze(sum(x(:,:,i),2)) - squeeze(sum(x1(:,:,i),2))+D0(:,i)+z(:,i)>=0];
end
C=[C,sum(sum(sum(eta1.*D0)))==r1*e-r2+r3];
C=[C,tao==lambda1*e-lambda2+lambda3];
C=[C,tao>=sum(sum(sum(eta1.*D0)))-beta*e];
for i=1:p
for j=1:t
C=[C,lambda*beta0+(lambda/(1-epsilon))*(tao0'*p0+(lambda20'+lambda30')*e*gam)+(1-lambda)*(theta*D0(i,j)*ones(1,m)*p0...
+(r20'+r30')*e*gam)>=eta*sum(x(i,:,j)+x1(i,:,j),2)];
end
end
for i=1:p
for j=1:t
C=[C,theta*D0(i,j)==r10*e-r20+r30];
end
end
C=[C,tao0==lambda10*e-lambda20+lambda30];
for i=1:p
for j=1:t
C=[C,tao0>=theta*D0(i,j)-beta0*e];
end
end
C=[C,cc+u >= sum(sum(sum((y1+y).*eo)))+sum(sum(z.*eh))+sum(sum(sum((x1+x).*ev))) + b*sum(sum(sum((x1+x),1),3).*dis)];
optimize(C,obj)
f(n)=obj;
end
l=round(lambda/0.2);
g(l)=mean(f);
h(l)=std(f);
end
value(h)
3 comentarios
Dyuman Joshi
el 22 de Feb. de 2023
I do not have the YALMIP toolbox right now to run your code.
However, you can certainly improve your code -
All the variables defined before x (first use of sdpvar) are constants, bring them out of the loops. (I do not know whether sdpvar variables are constants are not)
And it's not clear to me what is the purpose of the 2nd for loop (n as the loop variable), when there is no change in the code w.r.t n.
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