A Two-Level Metaheuristic for the Job-Shop Scheduling Problem with Multipurpose Machines

Table 2

Results on Rdata.

Instance	n, m	BKS	Best of TS [9]	Best of PSO [10]	Best of CP [20]	MPM-UPLA
						Best	Avg	No of Iters	Time
M6	6, 6	47	47	47	47	47	47.0	1	< 1 sec.
M10	10, 10	686	737	724	686	686	686.0	29	0:01:10
M20	20, 5	1022	1028	1025	1024	1022	1022.0	22	0:00:46
LA1	10, 5	(571)	574	574	573	571	571.0	1000	0:04:50
LA2	10, 5	529	535	534	534	530	530.8	1000	0:04:51
LA3	10, 5	477	481	480	478	478	478.0	1000	0:05:43
LA4	10, 5	502	509	506	504	502	502.8	878	0:04:31
LA5	10, 5	457	460	459	458	457	457.6	648	0:03:03
LA6	15, 5	799	801	800	799	799	799.0	15	0:00:13
LA7	15, 5	749	752	750	750	749	749.0	158	0:02:38
LA8	15, 5	765	767	767	766	765	765.0	183	0:02:54
LA9	15, 5	853	859	854	854	853	853.0	100	0:01:29
LA10	15, 5	804	806	806	805	804	804.0	274	0:03:55
LA11	20, 5	1071	1073	1072	1072	1071	1071.0	3	0:00:07
LA12	20, 5	936	937	936	936	936	936.0	2	0:00:04
LA13	20, 5	1038	1039	1039	1038	1038	1038.0	2	0:00:03
LA14	20, 5	1070	1071	1070	1071	1070	1070.0	4	0:00:07
LA15	20, 5	1089	1093	1090	1091	1089	1089.0	67	0:02:21
LA16	10, 10	717	717	732	717	717	717.0	2	0:00:05
LA17	10, 10	646	646	654	646	646	646.0	1	0:00:02
LA18	10, 10	666	674	694	666	666	666.0	72	0:02:49
LA19	10, 10	700	725	730	703	700	701.2	534	0:22:48
LA20	10, 10	756	756	756	757	756	756.0	1	0:00:03
LA21	15, 10	(829)	861	916	845	844	846.6	617	2:30:04
LA22	15, 10	(753)	790	839	775	772	774.6	754	2:30:05
LA23	15, 10	(832)	884	892	857	850	856.2	665	2:30:04
LA24	15, 10	(801)	825	870	818	821	823.0	723	2:30:06
LA25	15, 10	(782)	823	858	805	802	804.0	785	2:30:04
LA26	20, 10	(1059)	1086	1114	1074	1067	1068.2	218	2:30:31
LA27	20, 10	(1087)	1109	1141	1101	1095	1098.4	249	2:30:15
LA28	20, 10	(1077)	1097	1135	1084	1083	1086.0	227	2:30:07
LA29	20, 10	(996)	1016	1046	1006	1003	1004.6	258	2:30:08
LA30	20, 10	(1072)	1105	1148	1087	1087	1090.2	257	2:30:15
LA31	30, 10	⟶1520	1532	1549	1525	1520	1523.2	61	2:16:01
LA32	30, 10	(1658)	1668	1691	1664	1659	1660.4	65	2:30:56
LA33	30, 10	(1498)	1511	1530	1502	1498	1500.2	70	2:30:47
LA34	30, 10	(1536)	1542	1556	1542	1536	1537.2	70	2:31:01
LA35	30, 10	(1550)	1559	1577	1556	1551	1551.8	73	2:31:01
LA36	15, 15	1023	1054	1119	1034	1026	1033.0	213	2:30:17
LA37	15, 15	1062	1122	1190	1084	1084	1086.0	219	2:30:21
LA38	15, 15	954	1004	1063	973	976	976.0	249	2:30:18
LA39	15, 15	1011	1041	1131	1018	1024	1025.2	258	2:30:26
LA40	15, 15	955	1009	1057	984	977	984.0	169	2:30:33
ABZ5	10, 10	954	N/A	N/A	962	959	960.0	1000	0:48:07
ABZ6	10, 10	807	N/A	N/A	807	807	807.0	4	0:00:08
ABZ7	20, 15	(527)	N/A	N/A	544	547	548.8	57	2:32:24
ABZ8	20, 15	(540)	N/A	N/A	555	561	565.2	67	2:31:02
ABZ9	20, 15	(539)	N/A	N/A	562	555	565.2	68	2:31:18
CAR1	11, 5	(5035)	N/A	N/A	5057	5050	5053.4	1000	0:07:21
CAR2	13, 4	(5986)	N/A	N/A	5987	5986	5986.0	1000	0:05:45
CAR3	12, 5	(5623)	N/A	N/A	5626	5625	5629.2	1000	0:10:00
CAR4	14, 4	(6515)	N/A	N/A	6518	6515	6515.2	1000	0:07:25
CAR5	10, 6	5615	N/A	N/A	5764	5680	5691.4	1000	0:09:24
CAR6	8, 9	6147	N/A	N/A	6147	6147	6147.0	17	0:00:12
CAR7	7, 7	4425	N/A	N/A	4432	4425	4430.6	803	0:04:07
CAR8	8, 8	5692	N/A	N/A	5692	5692	5692.0	99	0:00:54
ORB1	10, 10	746	N/A	N/A	763	746	746.0	20	0:00:40
ORB2	10, 10	696	N/A	N/A	703	696	698.4	807	0:32:37
ORB3	10, 10	712	N/A	N/A	720	715	716.0	1000	0:44:29
ORB4	10, 10	753	N/A	N/A	753	753	753.0	8	0:00:22
ORB5	10, 10	639	N/A	N/A	643	639	639.0	325	0:15:04
ORB6	10, 10	754	N/A	N/A	766	754	754.0	145	0:05:35
ORB7	10, 10	302	N/A	N/A	302	302	303.4	866	0:38:49
ORB8	10, 10	639	N/A	N/A	651	641	641.0	1000	0:42:24
ORB9	10, 10	694	N/A	N/A	694	694	694.0	33	0:01:27
ORB10	10, 10	742	N/A	N/A	750	742	743.4	847	0:41:27