Piecewise Linear Model for Multiskilled Workforce Scheduling Problems considering Learning Effect and Project Quality

Table 4

Results for models.

Instance set	Gap-I	CPU (s)					Gap-II
		MNLP	MLP1	MLP2	MLP1+(31)	MLP2+(31)
1	0	404	0.53	0.52	0.53	0.38	0.08%
2	0	634	0.54	0.53	0.46	0.37	0.10%
3	0	479	0.52	0.44	0.44	0.41	0.06%
4	1.76%	7200	13.83	7.80	2.80	2.54	1.62%
5	1.55%	7200	10.47	14.35	3.19	3.40	1.47%
6	2.03%	7200	44.37	31.81	7.80	6.50	1.89%
7	1.57%	7200	5.57	6.80	1.78	1.72	1.48%
8	3.45%	7200	191.85	331.13	32.95	38.90	2.91%
9	4.32%	7200	1997.34	3446.71	35.75	34.95	4.19%
10	4.23%	7200	14.08	11.82	2.78	2.82	3.85%
11	4.27%	7200	6.04	7.30	2.25	2.31	3.80%
12	4.96%	7200	46.70	45.74	15.09	14.35	3.29%
13	0.04%	7200	39.69	13.28	1.71	1.28	0.05%
14	#	7200	23.82	14.83	2.11	1.76	0.30%
15	0	1352	21.59	9.42	0.86	0.59	0.00%
16	1.93%	7200	1.76	1.35	1.31	1.06	1.87%
17	1.64%	7200	1.93	1.48	1.69	1.39	1.05%
18	1.48%	7200	1.60	1.38	1.40	1.23	1.41%
19	2.66%	7200	132.45	115.70	6.52	5.55	1.75%
20	1.07%	7200	141.75	243.43	12.26	12.15	0.97%
21	1.65%	7200	135.26	163.46	6.53	5.62	0.85%
22	4.57%	7200	4.02	3.58	2.04	1.86	4.20%
23	6.06%	7200	7.89	7.27	2.24	2.27	2.74%
24	3.27%	7200	2.30	2.08	1.56	1.48	3.16%
25	#	7200	21.15	2.13	1.93	1.72	0.06%
26	0.07%	5293	22.82	11.79	2.26	1.98	0.81%
27	#	7200	30.96	12.61	4.52	4.07	0.16%
Average	2.19%	6168.96	108.18	166.99	5.73	5.65	1.63%