A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

Table 5

CPU time (seconds) for calculation of two algorithms.

Instance name	Algorithm	Maximum	Minimum	Mean	Standard deviation	Coefficient of variation
Perl 183-12×2	HGSAA	0.8424	0.4992	0.65156	0.085708	0.131543
	GA	0.9672	0.5304	0.6696	0.102803	0.153529
Gaskell 67-22×5	HGSAA	1.716	1.092	1.37436	0.169531	0.123353
	GA	2.1216	1.17	1.55688	0.272075	0.174756
Gaskell 67-36×5	HGSAA	3.7596	2.2776	3.2058	0.501429	0.156413
	GA	4.8984	2.5584	3.81108	0.760696	0.199601
Perl 183-55×15	HGSAA	12.0121	8.5957	9.91249	0.999262	0.100808
	GA	12.6517	9.8125	10.91837	0.846487	0.077529
Christofides 69-75×10	HGSAA	13.3225	10.1713	11.74674	1.162717	0.098982
	GA	13.7437	10.2805	12.30382	1.370948	0.111425
Perl 83-85×7	HGSAA	21.6373	17.0509	18.94194	1.305246	0.068908
	GA	24.3518	17.8621	21.04736	2.279007	0.10828
Christofides 69-100×10	HGSAA	33.2906	30.1859	31.36279	0.93519	0.029818
	GA	33.5558	30.6386	32.21258	0.962075	0.029866