Solving Capacitated Vehicle Routing Problem by an Improved Genetic Algorithm with Fuzzy C-Means Clustering

Table 2

Simulation results of the proposed algorithm.

Instance	BSK	BS		AVS		AVT/s
	Cost	Cost	Gap (%)	Cost	Gap (%)
A-n33-k5	661	661	0.0	666.25	0.79	0.24
A-n33-k6	742	743	0.13	749.1	0.96	0.15
A-n36-k5	799	799	0.0	800.05	0.13	0.14
A-n37-k6	949	955	0.63	986.75	3.98	0.3
A-n38-k5	730	730	0.0	747.05	2.34	0.16
A-n39-k5	822	822	0.0	848.0	3.16	0.26
A-n39-k6	831	855	0.48	864.5	4.03	0.19
A-n44-k7	937	937	0.0	984.2	5.04	0.33
B-n31-k5	672	672	0.0	683.1	1.65	0.15
B-n34-k5	788	788	0.0	798.35	1.31	0.19
B-n35-k5	955	955	0.0	974.3	2.02	0.17
B-n38-k6	805	805	0.0	822.1	2.12	0.24
B-n41-k6	829	832	0.36	853.5	2.96	0.24
B-n43-k6	742	742	0.0	785.55	2.23	0.26
B-n45-k5	751	755	0.53	768.85	2.38	0.36
B-n52-k7	747	744	0.0	766.2	2.57	0.42
P-n16-k8	450	450	0.0	450.0	0.0	0.1
P-n19-k2	212	212	0.0	212.0	0.0	0.02
P-n20-k2	216	216	0.0	216.0	0.0	0.03
P-n21-k2	211	211	0.0	211.0	0.0	0.04
P-n22-k2	216	216	0.0	216.0	0.0	0.04
P-n22-k8	590	590	0.0	590.0	0.0	0.05
P-n23-k8	529	529	0.4	531.1	0.4	0.16
P-n45-k5	510	515	5.85	539.85	5.85	0.29
P-n55-k8	588	590	5.48	620.25	5.48	0.55