Shared Mechanism-Based Self-Adaptive Hyperheuristic for Regional Low-Carbon Location-Routing Problem with Time Windows

Table 8

The effect of variations in clients’ time windows (η=1).

Instance	TC	DC	VC	FEC	PC	TD	TT	CE	MT
L25-1	1286.34	700	252	276.75	57.59	114.55	467.06	82.94	7.85
L25-2	957.35	400	246	254.95	56.40	107.22	460.02	76.41	12.61
L25-3	981.38	400	220	289.74	71.65	113.18	501.76	86.83	13.16
L50-1	1738.69	750	396	484.48	108.22	172.59	802.37	145.20	74.08
L50-2	2109.88	1200	362	460.86	87.02	164.55	744.64	138.12	98.63
L50-3	1816.81	900	406	423.62	87.19	154.81	721.23	126.96	65.22
L75-1	2211.91	950	592	538.70	131.21	184.90	1036.40	161.45	388.60
L75-2	2344.82	1100	576	545.84	122.98	208.88	1032.67	163.59	303.45
L75-3	2561.74	1250	532	637.82	141.92	223.06	1084.45	191.15	377.80
L100-1	3441.15	1750	728	801.71	161.43	276.57	1331.37	240.27	433.61
L100-2	3269.60	1300	842	915.17	212.42	316.53	1542.28	274.27	419.11
L100-3	2987.50	1150	806	835.06	196.44	282.89	1436.71	250.26	490.86
Average	2142.26	987.50	496.50	538.73	119.54	193.31	930.08	161.45	223.75
Median	2160.89	1025.00	469.00	511.59	115.60	178.74	917.52	153.32	201.04