Designing a Multistage Supply Chain in Cross-Stage Reverse Logistics Environments: Application of Particle Swarm Optimization Algorithms

Table 2

Experimental design results of GA with different groups of parameters.

GA
Generation	Population	Mutation rate	Crossover rate	Convergence time (S)	Execution time (S)	Objective function value
1000	10	0.02	0.6	45.65	65.22	585845.5
			0.95	36.91	52.72	586401.9
		0.05	0.6	58.88	84.12	582052.4
			0.95	60.12	85.88	585355.2
	20	0.02	0.6	99.07	141.53	585731.7
			0.95	58.49	83.57	579156.2
		0.05	0.6	111.26	158.94	578879.4
			0.95	94.15	134.51	578760.4
2000	10	0.02	0.6	59.42	112.12	583037.4
			0.95	51.87	97.88	587388.3
		0.05	0.6	90.85	171.42	576988.8
			0.95	89.87	169.58	580298.8
	20	0.02	0.6	88.23	166.49	576920.3
			0.95	72.51	136.81	579347.1
		0.05	0.6	155.42	293.72	575504.7
			0.95	133.79	252.45	576902.9