Expectation-Maximization Algorithm of Gaussian Mixture Model for Vehicle-Commodity Matching in Logistics Supply Chain

Table 4

The operation results of hybrid evolutionary algorithm in four cases.

	Situation 1			Situation 2			Situation 3			Situation 4
	Cost ($)	Error (%)	Time (s)	Cost ($)	Error (%)	Time (s)	Cost ($)	Error (%)	Time (s)	Cost ($)	Error (%)	Time (s)
1	392	4.25	271.16	330	2.55	330.19	305	2.04	363.21	379	1.43	381.37
2	401	3.33	283.54	801	2.00	337.85	481	1.60	371.63	321	1.12	390.21
3	492	4.87	290.39	472	2.92	348.47	103	2.34	383.31	310	1.64	402.48
4	556	4.97	265.88	603	2.98	314.26	494	2.39	345.68	506	1.67	362.97
5	803	5.61	277.56	430	3.37	335.47	529	2.69	369.02	181	1.88	387.47
6	537	4.26	283.17	235	2.56	336.20	678	2.04	369.82	627	1.43	388.32
Ave	530	4.55	278.62	479	2.73	333.74	432	2.18	367.11	387	1.53	385.47