Incorrect output of linear optimization problem
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Hello Everyone,
I'm trying to make a linear optimization problem over an objective function as follows which is called (objfun_36) to get the optimized value of the decision variable " zm " for each "s" which is called (zm_optimized(s)). However, I defined the decision varaible "zm" as a continous decsion varaible with llower bound zero and upper bound 1. I'm expecting the optimzed value of zm for each "s" to be a different value from 0 up to 1. However, I got zm_optimezed(s) equals 1 for all "s".. and this does not make sense.
Please can anyone help me ?
Subject to
Here is my code
ls=[20000,20000,20000,20000,20000,20000,20000,20000,20000,20000];
CK=[500,600,700,800,900,1000,1100,1200,1300,1400];
TN=10;
N_S=length(TN);
zm = optimvar('zm',N_S,'Type','continuous','LowerBound',0,'UpperBound',1);
CMRN=0.4*(10^5);
Rk=[2.7684,4.7962,6.0404,5.5868,5.2827,5.7736,6.1362,6.1943,5.9630,6.1183]*1.0e+08;
expo=1;
pL=zeros(1,TN);
for l=1:TN
pL(l)=l^-expo;
end
beta=pL./sum(pL);
veta_s=pL./sum(pL);
a = 500;
b = 2000;
Lks = ((b-a).*rand(10) + a);
aa = 0.1;
bb = 1;
Fkf= ((bb-aa).*rand(10,1) + aa)*(10^9);
k_=[1,1,1,1,1,1,1,1,1,1];
for k=1:1:TN
for s=1:1:TN
DRK_hat(k)=Lks(k,s)/Rk(k)
tks_hat(k,s)=(CK(k)*Lks(k,s))/Fkf(k)
eq_36(k,s)=zm*(-beta(k)*((DRK_hat(k)*veta_s(s)+(tks_hat(k,s)*veta_s(s))))-k_(k));
end
sumcol_36=sum(eq_36,1);
end
sumrows_36=sum(sumcol_36,2);
objfun_36=sumrows_36;
for s=1:TN
cache_location_constraint_36=(zm*ls(s))<=CMRN;
ProCach=optimproblem; % create an optimization problem
ProCach.Objective=objfun_36 %minimization equation 36
ProCach.Constraints.Constr1=cache_location_constraint_36;
%% optimal solver
opts=optimoptions('linprog');
[zm_optimized(s),fval,exitflag,output]=solve(ProCach,'Options',opts);
end
3 commentaires
Torsten
le 18 Nov 2022
so the constraint (11f) ensures that the sum of all services multipled by the length of each on should be less than the memory cach size..
Yes, but you only constrain one service at a time, not the sum of them.
Réponse acceptée
Matt J
le 18 Nov 2022
Modifié(e) : Matt J
le 18 Nov 2022
ls=[20000,20000,20000,20000,20000,20000,20000,20000,20000,20000];
CK=[500,600,700,800,900,1000,1100,1200,1300,1400];
TN=10;
N_S=TN;
zm = optimvar('zm',N_S,'Type','continuous','LowerBound',0,'UpperBound',1);
CMRN=0.4*(10^5);
Rk=[2.7684,4.7962,6.0404,5.5868,5.2827,5.7736,6.1362,6.1943,5.9630,6.1183]*1.0e+08;
expo=1;
pL=zeros(1,TN);
for l=1:TN
pL(l)=l^-expo;
end
beta=pL./sum(pL);
veta_s=pL./sum(pL);
a = 500;
b = 2000;
Lks = ((b-a).*rand(10) + a);
aa = 0.1;
bb = 1;
Fkf= ((bb-aa).*rand(10,1) + aa)*(10^9);
k_=[1,1,1,1,1,1,1,1,1,1];
for k=1:1:TN
for s=1:1:TN
DRK_hat(k)=Lks(k,s)/Rk(k);
tks_hat(k,s)=(CK(k)*Lks(k,s))/Fkf(k);
eq_36(k,s)=(-beta(k)*((DRK_hat(k)*veta_s(s)+(tks_hat(k,s)*veta_s(s))))-k_(k));
end
end
objfun_36=sum(eq_36,1);
ProCach=optimproblem; % create an optimization problem
ProCach.Objective=objfun_36*zm; %minimization equation 36
ProCach.Constraints.Constr1=ls*zm<=CMRN;
%% optimal solver
opts=optimoptions('linprog');
[sol,fval,exitflag,output]=solve(ProCach,'Options',opts);
zm_optimized=sol.zm
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