Corrigendum to “A Novel MILP Model for the Production, Lot Sizing, and Scheduling of Automotive Plastic Components on Parallel Flexible Injection Machines with Setup Common Operators”

Table 10

Experimental results for the MILP model for lot-sizing and scheduling on parallel flexible injection machines.

Dataset					Number of constraints	Number of variables	Number of binary variables	Number of integer variables	Number of continuous variables	Number of nonzeros	Objective value	GAP (%)	Termination condition	Time (sec)
S1	2	4	6	3	253	180	48	48	84	548	30700049.77	0.00	Optimal	0.1875
S2	4	6	8	3	529	360	144	144	72	1240	13400404.31	0.00	Optimal	0.0312
S3	6	8	16	3	1037	720	288	288	144	2672	11100863.25	0.00	Optimal	0.0156
S4	8	10	22	3	1617	1116	480	480	156	4372	8101150.62	0.00	Optimal	0.0781
M1	10	12	24	14	10882	7056	3360	3360	336	29232	75409783.76	0.00	Optimal	2.9056
M2	12	14	28	14	14630	9408	4704	4704	0	39872	162208156.70	0.00	Optimal	9.8883
M3	14	16	32	14	18930	12096	6272	6272	-448	52160	129012740.08	0.00	Optimal	19.5891
M4	16	18	36	14	23782	15120	8064	8064	-1008	66096	109311635.70	0.00	Optimal	24.0256
L1	18	20	40	14	29186	18480	10080	10080	-1680	81680	227015024.17	0.00	Optimal	2.0308
L2	20	40	60	14	61754	38640	22400	22400	-6160	164880	698925080.54	0.20	maxTimeLimit	18000.6893
L3	25	45	70	14	85269	53130	31500	31500	-9870	230910	542429122.73	0.00	Optimal	3786.1561
L4	30	50	80	14	112234	69720	42000	42000	-14280	307240	834529827.82	0.51	maxTimeLimit	18001.5215