wariant 1
param m :=5;
param n := 9;
set M :=1..m;
set N := 1..n;
param b {j in N};
param a {i in M, j in N};
param c {i in M};
var x {i in M, j in N} >= 0 integer;
var y {i in M} >=0 integer;
minimize koszt: sum{i in M}c[i]*y[i];
subject to OGR1 {i in M}:sum {j in N}x[i,j]=y[i];
subject to OGR2 {j in N}:sum {i in M}a[i,j]*x[i,j]>=b[j];
end;
data;
param b:=
1 12
2 10
3 12
4 10
5 30
6 15
7 12
8 15
9 25;
param c:=
1 300
2 175
3 195
4 155
5 120;
param a: 1 2 3 4 5 6 7 8 9 :=
1 350 0 15 10 50 11 40 15 0
2 10 100 0 0 15 150 0 40 40
3 15 25 150 25 20 20 300 10 25
4 0 10 0 150 35 0 50 100 20
5 25 0 15 0 150 15 30 0 150;
end;
Problem: lab4
Rows: 15
Columns: 50 (50 integer, 0 binary)
Non-zeros: 89
Status: INTEGER OPTIMAL
Objective: koszt = 1185 (MINimum) 146.2690476 (LP)
No. Row name Activity Lower bound Upper bound
------ ------------ ------------- ------------- -------------
1 koszt 1185
2 OGR1[1] 0 0 =
3 OGR1[2] 0 0 =
4 OGR1[3] 0 0 =
5 OGR1[4] 0 0 =
6 OGR1[5] 0 0 =
7 OGR2[1] 25 12
8 OGR2[2] 10 10
9 OGR2[3] 15 12
10 OGR2[4] 150 10
11 OGR2[5] 150 30
12 OGR2[6] 15 15
13 OGR2[7] 30 12
14 OGR2[8] 100 15
15 OGR2[9] 150 25
No. Column name Activity Lower bound Upper bound
------ ------------ ------------- ------------- -------------
1 x[1,1] * 0 0
2 x[1,2] * 0 0
3 x[1,3] * 0 0
4 x[1,4] * 0 0
5 x[1,5] * 0 0
6 x[1,6] * 0 0
7 x[1,7] * 0 0
8 x[1,8] * 0 0
9 x[1,9] * 0 0
10 x[2,1] * 0 0
11 x[2,2] * 0 0
12 x[2,3] * 0 0
13 x[2,4] * 0 0
14 x[2,5] * 0 0
15 x[2,6] * 0 0
16 x[2,7] * 0 0
17 x[2,8] * 0 0
18 x[2,9] * 0 0
19 x[3,1] * 0 0
20 x[3,2] * 0 0
21 x[3,3] * 0 0
22 x[3,4] * 0 0
23 x[3,5] * 0 0
24 x[3,6] * 0 0
25 x[3,7] * 0 0
26 x[3,8] * 0 0
27 x[3,9] * 0 0
28 x[4,1] * 0 0
29 x[4,2] * 1 0
30 x[4,3] * 0 0
31 x[4,4] * 1 0
32 x[4,5] * 0 0
33 x[4,6] * 0 0
34 x[4,7] * 0 0
35 x[4,8] * 1 0
36 x[4,9] * 0 0
37 x[5,1] * 1 0
38 x[5,2] * 0 0
39 x[5,3] * 1 0
40 x[5,4] * 0 0
41 x[5,5] * 1 0
42 x[5,6] * 1 0
43 x[5,7] * 1 0
44 x[5,8] * 0 0
45 x[5,9] * 1 0
46 y[1] * 0 0
47 y[2] * 0 0
48 y[3] * 0 0
49 y[4] * 3 0
50 y[5] * 6 0
Integer feasibility conditions:
INT.PE: max.abs.err. = 0.00e+000 on row 0
max.rel.err. = 0.00e+000 on row 0
High quality
INT.PB: max.abs.err. = 0.00e+000 on row 0
max.rel.err. = 0.00e+000 on row 0
High quality
End of output
Wariant 2
param m :=5;
param n := 9;
set M :=1..m;
set N := 1..n;
param b {j in N};
param a {i in M, j in N};
param c {i in M};
var x {i in M, j in N} >= 0 integer;
var y {i in M} >=0 integer;
maximize koszt: sum{i in M, j in N}a[i,j]*x[i,j];
subject to OGR1 {i in M}:sum {j in N}x[i,j]=y[i];
subject to OGR2 {j in N}:sum {i in M}a[i,j]*x[i,j]<=b[j];
subject to OGR3:sum{i in M}c[i]*y[i]<=1180;
end;
dane tak samo
roblem: lab41
Rows: 16
Columns: 50 (50 integer, 0 binary)
Non-zeros: 123
Status: INTEGER OPTIMAL
Objective: koszt = 95 (MAXimum) 141 (LP)
No. Row name Activity Lower bound Upper bound
------ ------------ ------------- ------------- -------------
1 koszt 95
2 OGR1[1] 0 0 =
3 OGR1[2] 0 0 =
4 OGR1[3] 0 0 =
5 OGR1[4] 0 0 =
6 OGR1[5] 0 0 =
7 OGR2[1] 10 12
8 OGR2[2] 0 10
9 OGR2[3] 0 12
10 OGR2[4] 0 10
11 OGR2[5] 30 30
12 OGR2[6] 15 15
13 OGR2[7] 0 12
14 OGR2[8] 15 15
15 OGR2[9] 25 25
16 OGR3 1140 1180
No. Column name Activity Lower bound Upper bound
------ ------------ ------------- ------------- -------------
1 x[1,1] * 0 0
2 x[1,2] * 0 0
3 x[1,3] * 0 0
4 x[1,4] * 0 0
5 x[1,5] * 0 0
6 x[1,6] * 0 0
7 x[1,7] * 0 0
8 x[1,8] * 1 0
9 x[1,9] * 0 0
10 x[2,1] * 1 0
11 x[2,2] * 0 0
12 x[2,3] * 0 0
13 x[2,4] * 0 0
14 x[2,5] * 2 0
15 x[2,6] * 0 0
16 x[2,7] * 0 0
17 x[2,8] * 0 0
18 x[2,9] * 0 0
19 x[3,1] * 0 0
20 x[3,2] * 0 0
21 x[3,3] * 0 0
22 x[3,4] * 0 0
23 x[3,5] * 0 0
24 x[3,6] * 0 0
25 x[3,7] * 0 0
26 x[3,8] * 0 0
27 x[3,9] * 1 0
28 x[4,1] * 0 0
29 x[4,2] * 0 0
30 x[4,3] * 0 0
31 x[4,4] * 0 0
32 x[4,5] * 0 0
33 x[4,6] * 0 0
34 x[4,7] * 0 0
35 x[4,8] * 0 0
36 x[4,9] * 0 0
37 x[5,1] * 0 0
38 x[5,2] * 0 0
39 x[5,3] * 0 0
40 x[5,4] * 0 0
41 x[5,5] * 0 0
42 x[5,6] * 1 0
43 x[5,7] * 0 0
44 x[5,8] * 0 0
45 x[5,9] * 0 0
46 y[1] * 1 0
47 y[2] * 3 0
48 y[3] * 1 0
49 y[4] * 0 0
50 y[5] * 1 0
Integer feasibility conditions:
INT.PE: max.abs.err. = 2.98e-013 on row 1
max.rel.err. = 3.11e-015 on row 1
High quality
INT.PB: max.abs.err. = 0.00e+000 on row 0
max.rel.err. = 0.00e+000 on row 0
High quality
End of output