A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints
Table 11
The results of numerical examples for MILP_T with hard and soft constraints.
Hard
1
2
846 (818)
1385
7
619
1
2
2
1
3
3
3
4
4
4
1
2
1015 (978)
1395
121
1584
2
2
5
5
3
10
7
4
15
15
1
3
1292 (1250)
1400
—
NFFS
—
2
4
—
3
10
—
4
15
—
1
3
1564 (1522)
1400
159
3675
3
2
10
10
3
15
14
4
30
30
Soft
1
2
851 (822)
1386
6
603
1
2
2
1
3
3
1
4
4
8
1
2
1020 (982)
1396
146
1568
3
2
5
3
3
10
16
4
15
17
1
3
1297 (1254)
1401
510
2461
4
2
4
5
3
10
7
4
15
29
1
3
1569 (1526)
1401
89
3599
6
2
10
4
3
15
9
4
30
40
Soft
1
2
851 (822)
1386
5
619
1
2
2
1
3
3
1
4
4
8
1
2
1020 (982)
1396
227
1580
3
2
5
3
3
10
16
4
15
17
1
3
1297 (1254)
1401
203
2560
4
2
4
4
3
10
10
4
15
23
1
3
1569 (1526)
1401
263
3651
6
2
10
4
3
15
9
4
30
40
Soft
1
2
851 (822)
1386
8
619
1
2
2
1
3
3
3
4
4
4
1
2
1020 (982)
1396
185
1584
2
2
5
5
3
10
7
4
15
15
1
3
1297 (1254)
1401
383
2616
4
2
4
4
3
10
11
4
15
21
1
3
1569 (1526)
1401
318
3668
6
2
10
4
3
15
14
4
30
30
: mode of transport ( = ). : number of transportation units using transportation mode e. : number of used transportation units using transportation mode e. : penalty coefficient. NFFS: Not found feasible solution.
