Modeling and Optimization of Robust Scheduling Template Considering Equipment Failure

doi:10.16183/j.cnki.jsjtu.2019.146

Abstract

Abstract:

In order to solve the scheduling problem of aircraft moving assembly line in uncertainty environment, this paper proposes a perceptron-dependent fruit fly optimization algorithm(PDFOA) to generate the assembly scheduling template with strong robustness. The proposed PDFOA, taking the fruit fly optimization algorithm as the basis, designs narrow-field osphresis-search operation and narrow-field vision-search operation which is helpful for searching and choosing neighborhood solutions. Simultaneously, the PDFOA sets up a memory library to enhance the global search ability. Finally, simulation experiments are conducted using different testing samples on different job scales. The proposed PDFOA is compared with tabu search, the genetic algorithm and the immune particle swarm optimization algorithm. The results demonstrate the effectiveness of the PDFOA.

Key words: failure and repair of equipments, fruit fly optimization algorithm, proactive scheduling, sampling simulation, robust baseline scheduling

CLC Number:

TP29

FANG Jia, LU Zhiqiang. Modeling and Optimization of Robust Scheduling Template Considering Equipment Failure[J]. Journal of Shanghai Jiao Tong University, 2020, 54(12): 1278-1290.

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References 17

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作业规模	实验组别	PDFOA		TS		GAP/%	R
作业规模	实验组别	Z	t^al/s	Z	t^al/s	GAP/%	R
30	1	3 257.742	21.05	3 153.854	19.30	－3.29	0.5
	2	2 971.501	23.16	3 231.603	19.40	8.75	0.6
	3	2 937.349	21.47	3 081.400	21.90	4.90	0.7
	4	3 039.011	23.45	3 098.801	30.10	1.97	0.7
	5	3 074.150	22.02	2 969.351	23.40	－3.53	0.5
Ave₃₀	—	3 055.951	22.23	3 107.002	22.82	1.67	—

作业规模	实验组别	PDFOA		TS		GAP/%	R
作业规模	实验组别	Z	t^al/s	Z	t^al/s	GAP/%	R
60	1	9 667.531	71.79	9 943.686	150.70	2.86	0.7
	2	9 551.523	67.62	9 831.644	137.70	2.93	0.6
	3	9 503.118	61.37	10 215.929	172.60	7.50	0.5
	4	9 724.105	66.95	10 082.333	175.20	3.68	0.6
	5	9 472.344	64.60	9 658.903	182.00	1.97	0.6
Ave₆₀	—	9 583.724	66.47	9 946.499	163.64	3.79	—
90	1	9 095.379	104.18	10 301.729	480.20	13.26	0.7
	2	9 589.933	94.70	9 793.311	457.90	2.12	0.7
	3	9 369.342	104.30	10 238.112	511.80	9.27	0.7
	4	9 777.214	96.57	10 317.234	474.10	5.52	0.7
	5	9 387.405	94.13	10 420.824	484.70	11.01	0.7
Ave₉₀	—	9 443.855	98.78	10 214.242	481.74	8.16	—
120	1	10 591.995	171.44	11 060.492	1 245.05	4.42	0.6
	2	9 508.043	179.99	10 999.779	1 305.17	15.69	0.8
	3	10 152.130	181.57	11 028.171	1 245.05	8.63	0.7
	4	10 056.386	174.82	10 823.794	1 542.50	7.63	0.6
	5	9 987.023	171.44	10 669.859	1 343.85	6.84	0.5
Ave₁₂₀	—	10 059.120	175.85	10 916.419	1 336.32	8.52	—

作业规模	IPSO		GA		PDFOA
作业规模	Z	t^al/s	Z	t^al/s	Z	t^al/s
30	4 185.22	16 723.950	3 642.85	17 010.340	3 232.68	21.483
60	18 313.33	57 523.320	18 186.85	65 243.440	12 090.34	99.285
90	13 853.76	97 861.270	12 472.02	109 085.600	9 693.10	128.982
120	14 272.08	115 713.200	11 923.75	124 381.700	9 718.58	211.281

作业规模	组别	PDFOA		GA		IPSO		GAP		R
作业规模	组别	Z	t^al/s	t^al/s	Z	t^al/s	GAP₁/%	GAP₂/%	R₁	R₂
30	1	3 059.399	28.015	4 069.015	4.603	4 138.863	3.945	33.00	35.28	1.0	1.0
	2	3 151.645	27.220	4 107.190	4.065	4 138.760	4.489	30.32	31.32	1.0	1.0
	3	3 167.558	26.129	3 985.915	4.342	4 139.131	4.508	25.84	30.67	1.0	1.0
	4	3 031.079	26.523	4 038.459	4.083	4 196.570	4.220	33.24	38.45	1.0	1.0
	5	2 986.407	26.062	4 122.606	3.976	4 142.255	4.389	38.05	38.70	1.0	1.0
Ave	—	3 079.218	26.790	4 064.637	4.214	4 151.116	4.310	32.00	34.81	—	—
60	1	9 604.442	72.216	12 558.948	9.042	12 512.393	8.810	30.76	30.28	1.0	1.0
	2	9 534.382	73.693	12 559.403	9.625	12 619.900	9.955	31.73	32.36	0.9	1.0
	3	9 912.964	74.979	12 586.081	9.256	12 299.707	10.983	26.97	24.08	1.0	1.0
	4	9 496.693	69.484	12 373.219	8.824	12 783.132	10.156	30.29	34.61	0.9	1.0
	5	9 187.852	66.451	12 348.720	8.190	12 726.143	10.094	34.40	38.51	1.0	1.0
Ave	—	9 547.267	71.364	12 485.274	8.987	12 588.255	9.999	30.77	31.85	—	—
90	1	9 378.256	124.456	12 854.942	12.674	13 055.721	13.711	37.07	39.21	1.0	1.0
	2	9 470.230	122.534	12 480.518	12.915	12 523.518	16.238	31.79	32.24	1.0	1.0
	3	9 279.456	99.309	12 680.626	12.744	13 515.244	16.766	36.65	45.65	1.0	1.0
	4	9 466.363	112.793	12 597.001	12.887	13 158.924	16.244	33.07	39.01	1.0	1.0
	5	9 592.995	96.299	12 657.266	12.112	12 967.308	16.212	31.94	35.17	1.0	1.0
Ave	—	9 437.460	111.078	12 654.071	12.667	13 044.143	15.834	34.08	38.22	—	—
120	1	10 689.500	196.357	14 936.332	20.671	14 488.041	22.008	39.73	35.54	1.0	1.0
	2	9 540.459	236.136	14 160.884	21.286	14 135.028	26.320	48.43	48.16	1.0	1.0
	3	10 724.056	184.227	14 244.757	20.777	14 742.373	26.744	32.83	37.47	1.0	0.9
	4	10 505.718	206.063	14 654.288	19.446	15 414.342	26.820	39.49	46.72	1.0	1.0
	5	9 984.351	189.940	14 123.134	18.927	14 777.539	26.614	41.45	48.01	1.0	1.0
Ave	—	10 288.817	202.544	14 423.879	20.222	14711.465	25.701	40.19	42.98	—	—