Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles

doi:10.21629/JSEE.2018.05.08

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (5): 953-968.doi: 10.21629/JSEE.2018.05.08

• Systems Engineering • Previous Articles Next Articles

Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles

Jiaming ZHANG*(), Zhong LIU(), Jianmai SHI(), Chao CHEN()

Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China

Received:2017-03-13 Online:2018-10-26 Published:2018-11-14
Contact: Jiaming ZHANG E-mail:zjm08091018@163.com;phillipliu@263.net;jianmaishi@gmail.com
About author:ZHANG Jiaming was born in 1982. He received his B.S. degree in communication command from National University of Defense Technology, Changsha, Hunan, China, in 2004. From 2004 to 2008, he was a commanding officer in combat troops. He received his M.S. degree in military training from National University of Defense Technology, in 2010. He is currently pursuing his Ph.D. degree in management science and engineering at National University of Defense Technology. His current research interests include task assignment, mission planning and supply chain management. E-mail: zjm08091018@163.com|LIU Zhong is a professor of the National University of Defense Technology, Changsha, Hunan, China, and the deputy director of Science and Technology on Information Systems Engineering Laboratory as well as a senior advisor of the Research Center for Computational Experiments and Parallel Systems at National University of Defense Technology. He received his M.S. and Ph.D. degrees from National University of Defense Technology, Changsha, China, in 1997 and 2000, respectively. His main research interests include planning systems, computational organization and intelligent systems. E-mail: phillipliu@263.net|SHI Jianmai is an associate research fellow of the National University of Defense Technology, Changsha, Hunan, China, and a researcher of Science and Technology on Information Systems Engineering Laboratory. His main research interests include task assignment, mission planning and supply chain management. E-mail: jianmaishi@gmail.com|CHEN Chao was born in 1977. He received his M.S. and Ph.D. degrees from National University of Defense Technology in 2003 and 2007 respectively. He is now an associate research fellow in National University of Defense Technology. His research interests include complexity of war systems, battle modeling and simulation, and automatization in command and control system. E-mail: jianmaishi@gmail.com
Supported by:
the National Natural Science Foundation of China(71471175);the National Natural Science Foundation of China(71471174);This work was supported by the National Natural Science Foundation of China (71471175; 71471174)

Abstract

Abstract:

Unmanned combat air vehicles (UCAVs) mission planning is a fairly complicated global optimum problem. Military attack missions often employ a fleet of UCAVs equipped with weapons to attack a set of known targets. A UCAV can carry different weapons to accomplish different combat missions. Choice of different weapons will have different effects on the final combat effectiveness. This work presents a mixed integer programming model for simultaneous weapon configuration and route planning of UCAVs, which solves the problem optimally using the IBM ILOG CPLEX optimizer for simple missions. This paper develops a heuristic algorithm to handle the medium-scale and large-scale problems. The experiments demonstrate the performance of the heuristic algorithm in solving the medium scale and large scale problems. Moreover, we give suggestions on how to select the most appropriate algorithm to solve different scale problems.

Key words: unmanned combat air vehicles (UCAVs), mission planning, route planning, weapon configuration, time windows

Jiaming ZHANG, Zhong LIU, Jianmai SHI, Chao CHEN. Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles[J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 953-968.

Figures/Tables 26

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Table 3

Table 4

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Fig 12

Table 5

Table 6

Results for 10 targets (Iterations=5 000)"

Number of instances	Number of hanging points	Weight of bomb load/kg	CPLEX result			Heuristic optimization methods result (before adding optimization strategy)			Heuristic optimization methods result (after adding optimization strategy)
Number of instances	Number of hanging points	Weight of bomb load/kg	Number of UCAVs used	Objective value	Computation time/s	Number of UCAVs used	Objective value	Computation time/s	Number of UCAVs used	Objective value	Computation time/s
1	4	600	3	308.41	1 528.62	4	397.01	5.64	3	396.05	4.17
	6	900	2	279.40	1 818.49	3	298.07	6.50	3	289.06	3.52
	8	1 200	2	244.95	2 178.99	2	269.94	4.75	2	265.17	5.02
2	4	600	3	327.98	1 559.41	4	354.61	5.03	4	309.17	5.37
	6	900	2	315.53	1 762.04	3	288.59	7.76	3	247.77	4.37
	8	1 200	2	212.41	2 100.98	2	267.88	4.91	2	249.27	5.13
3	4	600	3	327.70	1 565.23	4	433.97	5.63	3	423.90	3.15
	6	900	2	305.30	1 797.92	3	307.26	6.94	2	270.25	5.56
	8	1 200	2	266.72	2 035.51	2	270.59	3.48	2	256.35	3.16
4	4	600	3	320.43	1 545.68	4	416.76	4.47	3	413.57	5.61
	6	900	2	283.89	1 820.99	3	304.52	6.99	3	299.16	5.04
	8	1 200	2	251.63	2 036.52	2	286.43	4.58	2	231.08	3.45
5	4	600	3	310.30	1 588.34	4	389.30	5.77	4	343.51	3.15
	6	900	2	293.98	1 826.54	3	304.15	6.41	3	255.31	3.67
	8	1 200	2	263.50	2 010.42	2	308.78	7.01	2	272.33	3.79
6	4	600	3	323.11	1 571.59	4	406.91	6.71	3	351.19	4.51
	6	900	2	297.26	1 792.01	3	271.29	4.74	2	263.60	3.86
	8	1 200	2	235.56	2 045.99	2	282.55	4.71	2	278.42	5.51
7	4	600	3	331.15	1 738.78	4	463.52	7.24	4	439.97	3.06
	6	900	2	289.88	1 768.12	3	361.89	5.19	3	302.14	3.31
	8	1 200	2	274.67	2 008.25	2	304.19	6.97	2	290.32	4.95
8	4	600	3	357.66	1 782.41	4	400.19	6.66	4	320.95	4.44
	6	900	2	304.60	1 800.62	3	323.21	4.29	2	275.63	3.52
	8	1 200	2	211.37	2 034.13	2	283.22	5.58	2	282.48	5.07
9	4	600	3	370.25	1 706.42	5	448.21	5.65	4	399.08	5.73
	6	900	2	279.99	1 849.68	3	317.75	3.29	3	309.50	3.44
	8	1 200	2	265.34	2 090.53	2	254.60	3.68	3	247.79	5.99
10	4	600	3	333.53	1 586.89	5	473.34	5.43	4	398.82	3.29
	6	900	2	306.63	1 766.78	4	384.81	7.75	3	337.03	3.85
	8	1 200	2	253.85	2 141.24	2	232.33	7.93	2	193.33	5.21

Table 6

Table 7

Table 8

Table 9

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References 35

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Tgt^a	Dam^b	Des^c	Win^d/h	Pos^e
T1	0.85	Bridge	[2.14, 2.95]	(245.26, 240.39)
T2	0.81	Military camp	[3.07, 3.84]	(231.89, 291.20)
T3	0.85	Bunker	[4.37, 6.86]	(141.50, 200.05)
T4	0.84	Arsenal	[3.30, 4.59]	(356.32, 209.34)
T5	0.55	Airbase	[3.83, 4.62]	(315.56, 252.39)

Index	Parameter value
Cost/$ million	10
Payload capacity/kg	1 200
Full-loaded endurance/h	15
Cruise speed/(km/h)	180
Hanging points	8
Types of weapons	W1, W2 and W3

Weapon	Description	Weight/kg	Cost/$ thousand
W1	Small smart bombs	75	68
W2	Small precision guided bombs	165	84
W3	Laser-guided bombs	240	22

Weapon	T1	T2	T3	T4	T5
W1	0.82	0.84	0.43	0.34	0.02
W2	0.86	0.88	0.54	0.67	0.35
W3	0.93	0.95	0.85	0.75	0.56

Feature		Index	Parameter value
UCAV		Payload capacity of the UCAV/kg	600, 900, 1 200
		Number of hanging points	4, 6, 8
		Type of weapons	W1, W2 and W3
		Cruise speed /(km/h)	180
Weapons	W1	Weight/kg	75
	W1	Cost /$ thousand	68
	W2	Weight/kg	165
	W2	Cost /$ thousand	84
	W3	Weight/kg	240
	W3	Cost /$ thousand	22

Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles

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Number of instances	Number of hanging points	Weight of bomb load/kg	Heuristic optimization methods result (before adding optimization strategy)			Heuristic optimization methods result (after adding optimization strategy)
Number of instances	Number of hanging points	Weight of bomb load/kg	Number of UCAVs used	Objective value	Computation time/s	Number of UCAVs used	Objective value	Computation time/s
11	4	600	10	974.73	13.87	9	670.34	13.56
	6	900	6	720.32	13.04	6	688.95	13.03
	8	1 200	3	481.06	14.78	4	479.81	12.16
12	4	600	8	850.96	14.50	9	606.09	12.20
	6	900	6	664.56	15.28	6	468.31	14.81
	8	1 200	4	625.95	12.15	3	361.68	10.26
13	4	600	9	956.22	12.66	11	783.47	12.60
	6	900	6	745.49	11.49	6	412.36	9.76
	8	1 200	4	563.98	13.61	4	410.71	12.76
14	4	600	10	950.95	15.88	9	805.08	13.19
	6	900	5	638.65	13.88	6	506.17	13.01
	8	1 200	3	479.81	11.83	4	548.63	10.85
15	4	600	9	853.74	11.84	8	551.77	9.71
	6	900	6	667.08	11.83	6	477.80	10.29
	8	1 200	4	582.77	12.01	4	480.80	10.93
16	4	600	7	718.97	12.77	7	497.88	11.62
	6	900	6	667.34	12.48	6	428.88	11.20
	8	1 200	3	447.61	13.37	3	404.05	13.12
17	4	600	9	840.24	10.88	8	643.66	9.93
	6	900	6	675.44	10.98	6	484.96	9.59
	8	1 200	4	568.92	13.26	4	422.01	10.79
18	4	600	8	847.41	13.90	8	525.47	11.72
	6	900	6	660.60	15.31	6	454.85	13.59
	8	1 200	3	518.98	12.89	3	345.91	11.65
19	4	600	7	764.53	14.10	8	679.74	13.12
	6	900	5	603.78	20.73	5	392.81	20.14
	8	1 200	3	529.89	11.44	3	429.58	9.97
20	4	600	8	813.40	12.38	8	762.84	10.83
	6	900	5	581.59	14.12	5	459.30	12.91
	8	1 200	3	494.18	13.72	3	319.86	12.39

Number of instances	Number of hanging points	Weight of bomb load/kg	Heuristic optimization methods result (before adding optimization strategy)			Heuristic optimization methods result (after adding optimization strategy)
Number of instances	Number of hanging points	Weight of bomb load/kg	Number of UCAVs used	Objective value	Computation time/s	Number of UCAVs used	Objective value	Computation time/s
21	4	600	20	3 118.83	20.89	16	1 794.39	13.73
	6	900	15	2 409.37	22.10	10	1 218.02	10.61
	8	1 200	10	2 264.81	21.67	6	1 649.30	11.39
22	4	600	20	3 189.04	22.16	16	1 560.80	41.19
	6	900	13	2 220.32	22.02	8	1 518.78	18.40
	8	1 200	11	2 416.54	22.82	7	1 197.14	16.40
23	4	600	19	3 096.02	21.67	14	2 251.81	11.86
	6	900	15	2 388.66	21.69	9	1 542.54	20.90
	8	1 200	11	2 404.01	22.06	7	1 631.10	13.75
24	4	600	19	3 136.23	22.18	14	2 472.81	10.30
	6	900	15	2 471.75	22.21	9	1 436.20	16.86
	8	1 200	11	2 472.17	21.39	7	1 101.81	21.84
25	4	600	18	2 766.77	22.18	12	1 676.03	31.51
	6	900	15	2 097.54	21.40	10	1 499.16	26.55
	8	1 200	10	2 258.25	21.66	7	1 297.40	10.92
26	4	600	19	3 081.02	21.41	13	1 779.37	16.22
	6	900	15	2 543.91	22.25	10	1 433.83	15.15
	8	1 200	9	2 110.61	22.32	7	1 850.77	10.45
27	4	600	20	3 170.16	22.32	15	1 876.65	11.56
	6	900	14	2 392.31	21.48	9	1 613.56	17.49
	8	1 200	9	2 159.92	22.13	7	1 241.95	11.86
28	4	600	22	3 440.64	22.13	18	2 311.71	24.28
	6	900	14	2 381.41	22.22	10	1 250.07	13.28
	8	1 200	10	2 400.32	21.71	8	1 102.32	16.40
29	4	600	19	3 067.64	21.19	14	1 943.43	12.17
	6	900	15	2 444.17	21.87	10	1 458.42	15.62
	8	1 200	9	2 159.54	22.33	8	1 665.02	19.83
30	4	600	19	2 968.83	20.62	14	1 794.73	19.17
	6	900	13	2 288.42	22.02	8	1 723.92	18.58
	8	1 200	10	2 280.96	22.32	7	1 795.97	18.89

Number of instances	Number of hanging points	Weight of bomb load/kg	Heuristic optimization methods result (before adding optimization strategy)			Heuristic optimization methods result (after adding optimization strategy)
Number of instances	Number of hanging points	Weight of bomb load/kg	Number of UCAVs used	Objective value	Computation time/s	Number of UCAVs used	Objective value	Computation time/s
31	4	600	67	11 373.55	41.44	56	7 797.37	21.11
	6	900	54	9 701.86	43.46	41	5 810.26	27.30
	8	1 200	34	7 442.35	42.02	20	3 458.04	57.86
32	4	600	69	11 949.95	42.43	57	6 434.11	22.15
	6	900	54	10 287.85	41.46	46	5 400.13	15.60
	8	1 200	32	6 934.83	42.71	25	4 993.74	18.23
33	4	600	64	10 784.83	40.28	49	6 173.95	22.28
	6	900	54	9 580.61	42.67	36	4 977.12	27.61
	8	1 200	33	6 999.56	41.32	18	3 995.80	32.60
34	4	600	61	10 449.04	40.15	54	7 062.17	47.60
	6	900	45	8 258.14	42.67	41	5 068.17	28.24
	8	1 200	33	6 967.72	41.33	23	3 867.36	37.28
35	4	600	59	10 108.06	42.73	53	5 661.16	49.45
	6	900	50	9 240.44	43.26	42	4 852.35	62.38
	8	1 200	29	6 377.50	40.97	20	3 983.05	17.29
36	4	600	68	11 788.17	41.44	58	7 691.36	26.83
	6	900	56	10 239.31	41.32	47	6 160.87	27.12
	8	1 200	30	6 657.11	41.56	21	4 531.98	14.82
37	4	600	66	11 252.33	42.85	53	6 633.85	33.72
	6	900	52	9 395.75	42.87	43	4 875.10	23.40
	8	1 200	33	7 020.33	41.18	24	4 264.19	46.18
38	4	600	66	11 330.95	41.56	57	6 428.60	33.59
	6	900	53	9 657.26	42.40	41	6 024.72	30.25
	8	1 200	31	6 806.46	42.82	18	3 607.42	25.12
39	4	600	64	10 816.15	42.47	55	6 944.39	13.28
	6	900	51	9 158.53	43.66	40	4 685.69	48.52
	8	1 200	34	7 249.38	40.25	19	4 287.20	44.00
40	4	600	67	11 238.58	42.84	55	6 157.49	12.18
	6	900	51	9 485.01	41.19	38	5 988.15	65.37
	8	1 200	29	6 295.19	43.45	20	3 318.83	29.62