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	I	J	K	T	Number of constraints	Number of variables	Number of binary variables	Number of integer variables	Number of continuous variables	Number of nonzeros	Objective value	Termination condition	Time (sec)	GAP (%)
S1	2	4	6	3	253	180	48	48	84	548	30699918	Optimal	0.15767312	0.00
S2	4	6	8	3	529	360	144	144	72	1240	13400191	Optimal	0.13881159	0.00
S3	6	8	16	3	1037	720	288	288	144	2672	11100467	Optimal	0.13907146	0.00
S4	8	10	22	3	1617	1116	480	480	156	4372	8100579	Optimal	0.13229871	0.00
M1	10	12	24	14	10882	7056	3360	3360	336	29232	75400153.9	Optimal	12.6211112	0.00
M2	12	14	28	14	14630	9408	4704	4704	0	39872	162199368	Optimal	11.8187988	0.00
M3	14	16	32	14	18930	12096	6272	6272	−448	52160	129000642	Optimal	6.2376368	0.00
M4	16	18	36	14	23782	15120	8064	8064	−1008	66096	109300097	Optimal	12.3485208	0.00
L1	18	20	40	14	29186	18480	10080	10080	−1680	81680	226999024	Optimal	44.0416777	0.00
L2	20	40	60	14	61754	38640	22400	22400	−6160	164880	698898867	maxTimeLimit	10801.9226	0.53
L3	25	45	70	14	85269	53130	31500	31500	−9870	230910	889697348	maxTimeLimit	18000.7021	1.23
L4	30	50	80	21	169221	104580	63000	63000	−21420	462680	764199835	maxTimeLimit	18025.0699	0.32