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 11

Experimental results for the MILP model for lot sizing and scheduling on parallel flexible injection machines with setup common operators.

Dataset	I	J	K	L	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	2	3	392	228	96	96	36	880	30700163	Optimal	0.09	0.00
S2	4	6	8	2	3	904	504	288	288	−72	2224	9700582	Optimal	1.41	0.00
S3	6	8	16	2	3	1760	1008	576	576	−144	4736	23500652	Optimal	0.16	0.00
S4	8	10	22	2	3	2800	1596	960	960	−324	7860	18901051	Optimal	0.16	0.00
M1	10	12	24	4	14	36744	17136	13440	13440	−9744	100032	67803541	Optimal	19.20	0.00
M2	12	14	28	4	14	50596	23520	18816	18816	−14112	138992	99203043	Optimal	13.08	0.00
M3	14	16	32	4	14	66656	30912	25088	25088	−19264	184320	143003146	Optimal	39.02	0.00
M4	16	18	36	4	14	84924	39312	32256	32256	−25200	236016	146304008	Optimal	77.73	0.00
L1	18	20	40	6	14	156200	68880	60480	60480	−52080	432320	211505838	Optimal	208.83	0.00
L2	20	40	60	8	14	454244	195440	179200	179200	−162960	1212080	878439687	maxTimeLimit	10816.94	3.05
L3	25	45	70	12	14	950246	399630	378000	378000	−356370	2545660	912365839.8	maxTimeLimit	18032.57	4.47
L4	30	60	120	15	14	1893720	791280	756000	756000	−720720	5256960	1367518374	maxTimeLimit	10901.30	5.11