基于HNPM的有人/无人协同柔性火力资源调度研究

doi:10.12305/j.issn.1001-506X.2026.06.15

Abstract

Abstract:

To solve the problem of limited use of manned/unmanned formations in future warfare, a multi machine collaborative dynamic weapon target allocation model based on flexible job shop scheduling (FJSS) is constructed. Firstly, a collaborative combat mode is established with manned aerial vehicles as lead aircraft and multiple unmanned aerial vehicles as arsenal aircraft. Secondly, by combining the global optimization capability of nested segmentation algorithm with the local search capability of simulated annealing algorithm, a hybrid nested partitions method (HNPM) is proposed. The proposed method can be targeted at specific scenarios, unify scheduling and decision-making management of limited multi unmanned wingman missile resources within a specific time window, complete dynamic target allocation, and achieve effective tactical killing of multiple targets. The proposed method is of great significance in the field of dynamic weapon target allocation.

Key words: manned/unmanned teaming, dynamic weapon target allocation, flexible job shop scheduling, nested partitions method

CLC Number:

V 279

Kaifang WAN, Haozhi QIANG, Yunhui WU, Zhilin WU, Bo LI, Yongling FAN. HNPM-based manned/unmanned coordination flexible fire resource scheduling study[J]. Systems Engineering and Electronics, 2026, 48(6): 1933-1945.

Figures/Tables 14

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Fig.5

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Table 1

Fig.7

Table 2

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Table 6

Table 7

References 31

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EP_ikt	i=1				i=2				i=3				i=4
EP_ikt	t =1	t=2	t=3	t=4	t=1	t=2	t=3	t=4	t=1	t=2	t=3	t=4	t=1	t=2	t=3	t=4
k=1	0.156	0.264	0.313	0.154	0.128	0.273	0.204	0.172	0.110	0.320	0.134	0.090	0.100	0.156	0.264	0.313
k=2	0.185	0.398	0.310	0.198	0.123	0.383	0.323	0.204	0.116	0.325	0.179	0.097	0.162	0.185	0.398	0.310
k=3	0.360	0.348	0.408	0.324	0.152	0.309	0.316	0.336	0.161	0.393	0.332	0.180	0.114	0.360	0.348	0.408
k=4	0.357	0.436	0.486	0.367	0.288	0.437	0.474	0.370	0.287	0.477	0.383	0.283	0.208	0.357	0.436	0.486
k=5	0.441	0.629	0.521	0.418	0.452	0.650	0.527	0.407	0.458	0.501	0.387	0.488	0.380	0.441	0.629	0.521
k=6	0.622	0.732	0.721	0.597	0.504	0.736	0.708	0.590	0.537	0.724	0.612	0.550	0.524	0.622	0.732	0.721
k=7	0.726	0.798	0.784	0.711	0.684	0.788	0.788	0.716	0.702	0.781	0.749	0.700	0.716	0.726	0.798	0.784
k=8	0.796	0.784	0.800	0.795	0.786	0.800	0.799	0.796	0.788	0.799	0.800	0.788	0.781	0.796	0.784	0.800
k=9	0.795	0.784	0.800	0.797	0.799	0.783	0.792	0.795	0.783	0.798	0.800	0.789	0.779	0.795	0.784	0.800
k=10	0.708	0.602	0.709	0.700	0.744	0.580	0.707	0.704	0.732	0.716	0.706	0.744	0.773	0.708	0.602	0.709

算法	样本量
算法	20	50	100	200	500	1000
SA	0343000001	3300200040	0303300004	4103000004	2300100004	0321100000
	0103100004	0100400014	2010302000	0402000201	4300200003	0433000003
	0020400203	3010200030	2021000004	2101000003	1400300004	0121000002
	1022400000	4201200000	4010000041	2030300004	0100100200	0420400400
NPM	4200030400	0103103000	3403100000	0100430002	4200100300	3030130000
	3100130000	3100004200	1122000000	1340200000	0330020004	0231000004
	4120000002	1424000000	3200203000	0433100000	3110400000	1414000000
	3210400000	2230040000	0014400004	0212000004	2124000000	2020402000
HNPM	0120420000	1030040004	0000300040	0130103000	0012300004	0100104300
	1100030003	0420300003	0000200320	0410020000	0343030000	4413000000
	2300100004	0120000001	1000200320	0312004000	0241020000	2310002000
	0404200000	3210000002	0100040010	0324040000	0211040000	2320040000

算法	样本量
算法	20	50	100	200	500	1000
SA	T₁: 0.933450	T₁: 0.953133	T₁: 0.936928	T₁: 0.913164	T₁: 0.915228	T₁: 0.940551
	T₂: 0.943544	T₂: 0.913246	T₂: 0.919452	T₂: 0.929295	T₂: 0.932335	T₂: 0.976977
	T₃: 0.933594	T₃: 0.911405	T₃: 0.904492	T₃: 0.933887	T₃: 0.905254	T₃: 0.914809
	T₄: 0.945124	T₄: 0.974165	T₄: 0.984463	T₄: 0.951125	T₄: 0.939677	T₄: 0.900684
NPM	T₁: 0.916654	T₁: 0.904473	T₁: 0.906777	T₁: 0.901763	T₁: 0.900289	T₁: 0.901795
	T₂: 0.901221	T₂: 0.902919	T₂: 0.902706	T₂: 0.905058	T₂: 0.903272	T₂: 0.900275
	T₃: 0.909623	T₃: 0.917665	T₃: 0.902197	T₃: 0.902147	T₃: 0.901766	T₃: 0.907628
	T₄: 0.905248	T₄: 0.905619	T₄: 0.909579	T₄: 0.905230	T₄: 0.907408	T₄: 0.900117
HNPM	T₁: 0.902541	T₁: 0.903106	T₁: 0.901239	T₁: 0.901721	T₁: 0.901656	T₁: 0.901721
	T₂: 0.907328	T₂: 0.902532	T₂: 0.902036	T₂: 0.902371	T₂: 0.902422	T₂: 0.902639
	T₃: 0.904163	T₃: 0.905410	T₃: 0.905878	T₃: 0.904189	T₃: 0.905452	T₃: 0.900363
	T₄: 0.900725	T₄: 0.903325	T₄: 0.902679	T₄: 0.901795	T₄: 0.901348	T₄: 0.900595

算法	样本量
算法	20	50	100	200	500	1000
SA	0.103840	0.100241	0.0922045	0.0794917	0.0603326	0.050534
NPM	0.023551	0.019454	0.0140351	0.00919921	0.00754621	0.00636420
HNPM	0.010384	0.009463	0.007459	0.006640	0.007148	0.003917

算法	样本量
算法	20	50	100	200	500	1000
SA	3.230019e-003	3.487035e-003	3.673289e-003	4.135442e-003	4.653664e-003	6.109156e-003
NPM	1.191283e-001	3.228989e-001	6.007138e-001	1.324621e+000	3.178222e+000	6.275431e+000
HNPM	4.133486e-001	7.049307e-001	1.052037e+000	1.473726e+000	2.143410e+000	3.117750e+000

HNPM-based manned/unmanned coordination flexible fire resource scheduling study

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Figures/Tables 14

References 31

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样本量算法	20	50	100	200	500	1000
PSO	0.098444	0.087108	0.078488	0.065785	0.051796	0.040963
IACO	0.012250	0.012040	0.010276	0.009465	0.007505	0.005223
HNPM	0.010384	0.009463	0.007459	0.006640	0.007148	0.003917

样本量算法	20	50	100	200	500	1000
PSO	2.841915e-001	4.129122e-001	7.891265e-001	1.264725e+000	1.806283e+000	2.920293e+000
IACO	1.075010e+000	1.213043e+000	1.734973e+000	2.404474e+000	3.237525e+000	4.239210e+000
HNPM	4.133486e-001	7.049307e-001	1.052037e+000	1.473726e+000	2.143410e+000	3.117750e+000