MILP Model and a Rolling Horizon Algorithm for Crane Scheduling in a Hybrid Storage Container Terminal

Table 2

Computational results for large-scale instances.

No.	n	CPLEX	IGA		RHA		Gap^1	Gap^2
		31	50	330.39	346.78	1726.18				298.02	410.82	9.80	14.06	76.20
32	50	267.69	256.25	1588.04	241.12	361.38	9.93	5.91	77.24
33	50	295.96	295.31	1877.48	272.04	452.41	8.08	7.88	75.90
34	50	334.29	353.70	1525.58	293.54	405.75	12.19	17.01	73.40
35	50	349.08	436.53	1818.01	319.23	405.36	8.55	26.87	77.70
36	50	289.87	291.69	1605.26	265.20	416.74	8.51	9.08	74.04
Average		311.21	330.04	1690.09	281.52	408.75	9.51	13.47	75.75
37	80	449.74	437.34	3234.23	389.17	1333.57	13.47	11.01	58.77
38	80	452.70	417.70	3174.24	380.19	1124.53	16.02	8.98	64.57
39	80	476.53	507.52	2895.86	424.92	1291.56	10.83	16.27	55.40
40	80	476.24	472.40	3136.88	414.14	1195.63	13.04	12.33	61.88
41	80	413.72	440.01	3482.26	358.67	1246.72	13.30	18.49	64.20
42	80	458.94	465.64	3104.30	360.62	1088.79	21.42	22.55	64.93
Average		454.64	456.77	3171.29	387.95	1213.47	14.68	14.94	61.62
43	110	796.92	740.54	4563.32	527.78	3094.35	33.77	28.73	32.19
44	110	635.96	606.28	4378.75	476.83	2769.33	25.02	21.35	36.76
45	110	765.26	711.67	4974.93	539.26	3085.58	29.53	24.23	37.98
46	110	709.63	608.44	4903.29	492.82	2640.43	30.55	19.00	46.15
47	110	801.08	844.62	4663.09	586.76	3042.50	26.75	30.53	34.75
48	110	676.89	632.56	4837.26	514.52	2788.55	23.99	18.66	42.35
Average		730.95	690.68	4720.11	522.99	2903.46	28.27	23.75	38.36
Average							17.49	17.39	58.58

Obj: objective function value; CPU: computation time (second).
Gap¹ = (-)/ (%).
Gap² = (-)/ (%).
= (-)/ (%).