Quartz | Level 8

## Mixed integer Linear Programing

Hello

I am getting optimal feasible solution as 31250 with decision variable values 0, 7.5 and 20.

proc optmodel;
var x{1..3}>=0;
max z=900*x[1]+1500*x[2]+1000*x[3];
con c1: 50000*x[1]+12000*x[2]+8000*x[3]<=250000;
con c2: 1*x[1]+0*x[2]+0*x[3]<=4;
con c3: 0*x[1]+1*x[2]+0*x[3]<=15;
con c4: 0*x[1]+0*x[2]+1*x[3]<=20;
solve with milp/presolver=none;
print x[1] x[2] x[3];
quit;

x1,x3>=0 or integer

x2>=0.

But in excel it is given as 42,250 optimal feasible solution with decision variable values 4, 4.16,0. Please help if I am going wrong.

1 ACCEPTED SOLUTION

Accepted Solutions
SAS Super FREQ

## Re: Mixed integer Linear Programing

You have not specified any integer variables.  Use the INTEGER option in the VAR statement and then the .RELAX suffix to relax x[2].  Also, you need not enter the zero coefficients explicitly.  In fact, you can avoid those last three constraints by instead using the .UB variable suffix:

``````proc optmodel;
var x{1..3}>=0 integer;
x[2].relax = 1;
max z=900*x[1]+1500*x[2]+1000*x[3];
con c1: 50000*x[1]+12000*x[2]+8000*x[3]<=250000;
x[1].ub = 4;
x[2].ub = 15;
x[3].ub = 20;
solve with milp/presolver=none;
print x;
quit;``````

If you change the objective coefficient for x[1] from 900 to 9000, you do get an optimal objective value of 42,250.

SAS Super FREQ

## Re: Mixed integer Linear Programing

You have not specified any integer variables.  Use the INTEGER option in the VAR statement and then the .RELAX suffix to relax x[2].  Also, you need not enter the zero coefficients explicitly.  In fact, you can avoid those last three constraints by instead using the .UB variable suffix:

``````proc optmodel;
var x{1..3}>=0 integer;
x[2].relax = 1;
max z=900*x[1]+1500*x[2]+1000*x[3];
con c1: 50000*x[1]+12000*x[2]+8000*x[3]<=250000;
x[1].ub = 4;
x[2].ub = 15;
x[3].ub = 20;
solve with milp/presolver=none;
print x;
quit;``````

If you change the objective coefficient for x[1] from 900 to 9000, you do get an optimal objective value of 42,250.

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