# An Improved Genetic Algorithm for Solving the Semi-Soft Clustered Vehicle Routing Problem
## 5 Results and Discussion
### Computational Experiments
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1 & A-n32-C11-V2-Q100 & 522 & 522(0) & \((-1.34\%)\)\(\otimes\)15(0) & 515.0 & 0\% & 515(0) & 515 \\
2 & A-n33-C11-V2-Q100 & 472 & 472(0) & \((04724+2.39\%)\) & 472.0 & 0\% & 461(0) & 461 \\
3 & A-n33-C11-V2-Q100 & 562 & 562(0) & \((-1.42\%)\)\(\otimes\)45(0) & 554.0 & 0\% & 554(0) & 554 \\
4 & A-n34-C12-V2-Q100 & 547 & 547(0) & \((-1.65\%)\)\(\otimes\)38(0) & 538.0 & 0\% & 538(0) & 538 \\
5 & A-n36-C12-V2-Q100 & 588 & 588(0) & \((-4.93\%)\)\(\otimes\)(\(2.99\%)\) & 599.0 & 0\% & 543(0) & 543 \\
6 & A-n37-C13-V2-Q1000 & 569(0) & \((-0.53\%)\)\(\otimes\)566(0) & \((-3.83\%)\) & 566.0 & 0\% & 545(0) & 545 \\
7 & A-n37-C13-V2-Q100 & 615 & 615(0) & \((-0.98\%)\)\(\otimes\)609(+0.66\%) & 610.8 & 0.30\% & 605(0) & 605 \\
8 & A-n38-C13-V2-Q100 & 507 & 507(0) & \((005070)\) & 507.0 & 0\% & 507(0) & 507 \\
9 & A-n39-C13-V2-Q100 & 610 & 610(0) & \((-1.48\%)\)\(\otimes\)601(2+2.21\%) & 601.0 & 0\% & 588(0) & 588 \\
10 & A-n39-C13-V2-Q100 & 613 & 613(0) & \((0613+1.66\%)\)\(\otimes\) & 613.0 & 0\% & 603(0) & 603 \\
11 & A-n41-C15-V2-Q100 & 714 & 714(0) & \((-3.22\%)\)\(\otimes\)91(0) & 693.3 & 0.33\% & 691(0) & 691 \\
12 & A-n45-C15-V3-Q100 & 712 & 712(0) & \((-1.54\%)\)\(\otimes\)71(0+7.52\%) & 701.0 & 0\% & 652(0) & 652 \\
13 & A-n45-C16-V3-Q100 & 664 & 664(0) & \((06644/0)\)\(\otimes\)(45) & 664.0 & 0\% & 661(0) & 661 \\
14 & A-n46-C16-V3-Q100 & 664 & 664(0) & \((-1.05\%)\)\(\otimes\)(\(2.34\%)\) & 657.0 & 0\% & 642(0) & 642 \\
15 & A-n48-C16-V3-Q100 & 683 & 683(0) & \((-0.44\%)\)\(\otimes\)60(0) & 680.0 & 0\% & 680(0) & 680 \\
16 & A-n53-C18-V3-Q100 & 651 & 651(0) & \((-3.69\%)\(\otimes\)27(0)\) & 627.0 & 0\% & 627(0) & 627 \\
17 & A-n54-C18-V3-Q100 & 724 & 724(0) & \((07244+3.58\%)\) & 724.0 & 0\% & 699(0) & 699 \\
18 & A-n55-C19-V3-Q100 & 653 & 653(0) & \((-1.23\%)\)\(\otimes\)645(0) & 647.4 & 0.3\% & 645(0) & 645 \\
19 & A-n60-C20-V3-Q100 & 787 & 787(0) & \((-1.52\%)\)\(\otimes\)775(+1.17\%) & 779.8 & 0.2\% & 762(0) & 762 \\
20 & A-n61-C21-V4-Q100 & 682 & 682(0) & \((-1.61\%)\)\(\otimes\)71(0) & 671.0 & 0\% & 671(0) & 671 \\
21 & A-n62-C21-V3-Q100 & 778 & 778(0) & \((07778+0.91\%)\) & 778.0 & 0\% & 771(0) & 771 \\
22 & A-n63-C21-V4-Q100 & 801 & 801(0) & \((-1.23\%)\)\(\otimes\)791(+1.54\%) & 791.0 & 0\% & 779(0) & 779 \\
23 & A-n63-C21-V3-Q100 & 865 & 865(0) & \((0865+3.35\%)\) & 865.0 & 0\% & 837(0) & 837 \\
24 & A-n64-C22-V3-Q100 & 773 & 773(0) & \((-0.78\%)\)\(\otimes\)76(0) & 767.0 & 0\% & 767(0) & 767 \\
25 & A-n65-C22-V3-Q100 & 725 & 725(0) & \((-0.69\%)\(\otimes\)72(0+3.90\%)\) & 720.0 & 0\% & 693(0) & 693 \\
26 & A-n69-C23-V3-Q100 & 814 & 814(0) & \((-1.11\%)\)\(\otimes\)805(+1.39\%) & 809.4 & 0.55\% & 794(0) & 794 \\
27 & A-n80-C27-V4-Q100 & 972 & 972(0) & \((-2.16\%)\)\(\otimes\)951(+0.74\%) & 951.0 & 0\% & 944(0) & 944 \\
28 & B-n31-C11-V2-Q100 & 375 & 375(0) & \((03750)\) & 375.0 & 0\% & 375(0) & 375 \\
29 & B-n34-C12-V2-Q100 & 416 & 416(0) & \((-0.24\%)\)\(\otimes\)415(0) & 415.0 & 0\% & 415(0) & 415 \\
30 & B-n35-C12-V2-Q100 & 562 & 562(0) & \((05624-0.90\%)\) & 562.0 & 0\% & 557(0) & 557 \\
31 & B-n83-C13-V2-Q100 & 431 & 431(0) & \((-0.93\%)\)\(\otimes\)427(0) & 427.0 & 0\%In addition, the meaning of the values in parentheses in the relevant columns in Table 1 is as follows: Column 4 (8) shows the difference between the results obtained by the proposed algorithm and the corresponding results in [11, 23] when solving the CluVRP (SoftCluVRP) instances. The values in the left (right) parentheses of Column 5 represent the difference between the results obtained using the proposed algorithm to solve the SemiSoftCluVRP and CluVRP (SoftCluVRP) instances.