A DE-LS Metaheuristic Algorithm for Hybrid Flow-Shop Scheduling Problem considering Multiple Requirements of Customers

Table 3

Computational results about objective.

Instances	Types	DE-LS		Without LS		Best/1		Current-to-best/1		Rand/1
		Obj	Min	Obj	Min	Obj	Min	Obj	Min	Obj	Min
1	J20-M3	104.68	104.68	105.25	104.68	105.44	104.68	105.15	104.68	104.79	104.68
2	J20-M4	79.60	79.60	80.47	79.60	81.17	79.60	80.11	79.60	79.90	79.60
3	J30-M3	134.63	134.46	137.30	134.99	137.90	134.99	136.51	134.46	136.14	134.98
4	J30-M4	120.55	120.51	121.53	120.53	123.46	122.37	121.71	120.56	120.88	120.51
5	J40-M3	196.20	195.89	198.10	196.51	201.96	198.00	199.40	196.59	198.28	196.70
6	J40-M4	155.23	154.96	157.66	155.62	158.81	156.54	156.12	155.28	155.89	154.96
7	J50-M3	229.65	229.08	230.94	229.05	232.62	230.61	231.07	229.25	230.16	229.21
8	J50-M4	207.54	206.65	209.93	207.63	213.70	210.03	208.81	207.32	208.56	207.11
9	J60-M3	283.95	282.97	286.76	284.36	289.78	286.25	285.62	284.11	286.50	283.53
10	J60-M4	255.86	254.68	257.96	255.77	265.17	261.83	261.88	256.21	260.33	257.66
11	J70-M3	324.68	321.56	329.79	325.87	334.77	328.82	328.82	325.60	332.79	330.23
12	J70-M4	337.72	336.66	343.81	340.52	351.49	345.41	346.60	343.06	345.64	342.46
13	J80-M3	414.80	411.70	423.99	418.48	431.32	424.06	423.16	419.02	429.11	420.77
14	J80-M4	362.54	359.00	367.54	364.58	370.72	365.28	369.02	361.06	372.58	364.22
15	J90-M3	399.48	398.22	400.91	399.24	403.39	399.32	400.82	398.44	405.90	402.07
16	J90-M4	366.55	364.21	373.06	367.38	379.74	374.52	375.61	372.49	384.60	377.17
17	J100-M3	432.39	430.50	434.83	433.20	439.23	437.19	435.94	430.04	443.80	438.62
18	J100-M4	463.80	460.39	470.86	465.35	477.93	467.42	471.54	463.94	477.54	471.25
Average		270.55	269.21	273.93	271.30	277.70	273.72	274.33	271.21	276.30	273.10