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

Table 4

Optimal objective function values of two algorithms.

Instance name	Algorithm	Maximum	Minimum	Mean	Standard deviation	Coefficient of variation
Perl 183-12×2	HGSAA	740420	157810	502684	176218.7	0.350555
	GA	1322000	219800	660282	234416.0	0.355024
Gaskell 67-22×5	HGSAA	1718000	1071600	1432110	213118.8	0.148815
	GA	3146500	1506200	2170660	553894.4	0.255173
Gaskell 67-36×5	HGSAA	3635500	1877000	2879510	561628.4	0.195043
	GA	3836900	2219400	2886460	582830.7	0.201919
Perl 183-55×15	HGSAA	4120700	3606800	3985110	152566.1	0.038284
	GA	4307000	3755100	4090590	185978.4	0.045465
Christofides 69-75×10	HGSAA	5562400	4290900	4826600	394532.4	0.081741
	GA	6359300	4859800	5418878	525869.4	0.097044
Perl 83-85×7	HGSAA	6529200	5050500	5693300	531113.4	0.093287
	GA	7057200	5545800	6283210	428004.8	0.068119
Christofides 69-100×10	HGSAA	5978900	5074600	5592260	332403.7	0.05944
	GA	6177500	5211900	5792190	354180.4	0.061148