Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

doi:10.23919/JSEE.2023.000005

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (1): 81-98.doi: 10.23919/JSEE.2023.000005

• SYSTEMS ENGINEERING • Previous Articles Next Articles

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

Jianmai SHI(), Jiaming ZHANG, Hongtao LEI, Zhong LIU, Rui WANG()

¹ College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Received:2021-02-15 Online:2023-02-18 Published:2023-03-03
Contact: Rui WANG E-mail:jshi1980@163.com;ruiwangnudt@gmail.com
About author:
SHI Jianmai was born in 1980. He received his Ph.D. degree in management science and engineering from National University of Defense Technology (NUDT), Changsha, China, in 2011. During 2008 to 2009, he was a visiting Ph.D. student with the University of Windsor, Canada. During 2014 to 2015, he was a visiting scholar at McGill University. He is now the professor at the College of System Engineering, NUDT, Changsha, China. His current research interests include vehicle routing, supply chain management and heuristic design. E-mail: jshi1980@163.com

ZHANG Jiaming was born in 1982. He received his B.S. degree in communication command from National University of Defense Technology (NUDT), Changsha, 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 NUDT, in 2010. He is currently pursuing his Ph.D. degree in management science and engineering at NUDT. His current research interests include task assignment, mission planning and supply chain management. E-mail: zjm08091018@163.com

LEI Hongtao was born in 1982. He received his B.S., M.S., and Ph.D. degrees in management science and engineering from National University of Defense Technology, Changsha, China, in 2004, 2006, and 2011, respectively. He was an academic visitor of CIRRELT with the University of Montreal, Canada, from 2009 to 2010. He is currently an associate professor with the College of Systems Engineering, National University of Defense Technology. His research interests include resilient energy system planning, and microgrid robust design and optimization. E-mail: hongtaolei@aliyun.com

LIU Zhong was born in 1968. He received his Ph.D. degree in management science from National University of Defense Technology (NUDT), Changsha, China, in 2000. He is currently a professor with NUDT. He is also the vice-dean with the College of Systems Engineering Laboratory, NUDT, and a senior advisor with the Research Center for Computational Experiments and Parallel Systems, NUDT. His main research interests include planning systems, computational organization, and intelligent systems. E-mail: liuzhong@nudt.edu.cn

WANG Rui was born in 1986 He received his B.S. degree from National University of Defense Technology (NUDT), China, in 2008, and Ph.D. degree from the University of Sheffield, U.K. in 2013. He is currently an associate professor at the NUDT. His main research interests include evolutionary computation, multi-objective optimization, machine learning, optimization methods on energy internet network. E-mail: ruiwangnudt@gmail.com
Supported by:
This work was supportes by the National Nature Science Foundation of China (71771215; 62122093).

Abstract

Abstract:

Unmanned air vehicles (UAVs) have been regularly employed in modern wars to conduct different missions. Instead of addressing mission planning and route planning separately, this study investigates the issue of joint mission and route planning for a fleet of UAVs. The mission planning determines the configuration of weapons in UAVs and the weapons to attack targets, while the route planning determines the UAV’s visiting sequence for the targets. The problem is formulated as an integer linear programming model. Due to the inefficiency of CPLEX on large scale optimization problems, an effective learning-based heuristic, namely, population based adaptive large neighborhood search (P-ALNS), is proposed to solve the model. In P-ALNS, seven neighborhood structures are designed and adaptively utilized in terms of their historical performance. The effectiveness and superiority of the proposed model and algorithm are demonstrated on test instances of small, medium and large sizes. In particular, P-ALNS achieves comparable solutions or as good as those of CPLEX on small-size (20 targets) instances in much shorter time.

Key words: unmanned air vehicle (UAV), mission planning, routing, adaptive large neighborhood search

Jianmai SHI, Jiaming ZHANG, Hongtao LEI, Zhong LIU, Rui WANG. Joint mission and route planning of unmanned air vehicles via a learning-based heuristic[J]. Journal of Systems Engineering and Electronics, 2023, 34(1): 81-98.

Figures/Tables 16

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Fig 3

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Fig 6

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Table 1

Table 2

Table 3

Performance characteristics of CPLEX and P-ALNS tested using 10 targets instances"

Armament	Number	Gap/%	CPLEX		P-ALNS
Armament	Number	Gap/%	Obj. Val.	Time/s	Obj. Val.	Initial solution	Time/s	Improvement/%
Four hanging points & 600 kg payload capacity	1	1.36	2424597.98	0.12	2457572.51	3888657.08	14.13	36.80
	2	0.59	2380102.02	1.92	2394144.62	3873176.37	14.84	38.19
	3	1.46	2060957.84	0.20	2091047.82	3608727.74	13.65	42.06
	4	1.75	2352257.91	0.44	2393422.42	3724640.19	14.83	35.74
Four hanging points & 600 kg payload capacity	5	1.59	2114529.56	0.65	2148150.58	3777908.04	14.32	43.14
	6	1.39	2243336.12	0.53	2274518.49	4068900.50	14.45	44.10
	7	1.76	2155791.79	0.40	2193733.73	3751946.02	15.65	41.53
	8	0.84	2141811.63	0.34	2159802.85	3806186.38	15.54	43.26
	9	0.65	2041426.68	0.27	2054695.95	3776444.99	14.93	45.59
	10	0.77	2360302.65	0.55	2378476.98	3923007.34	15.92	39.37
Six hanging points & 900 kg payload capacity	11	0.28	2409043.58	1.79	2415788.90	4878869.49	15.18	50.48
	12	0.15	2451521.48	0.22	2455198.76	4672567.67	15.70	47.46
	13	1.27	2150123.74	0.20	2177430.31	4289204.62	14.47	49.23
	14	0.44	2223192.75	1.43	2232974.80	4544061.43	15.04	50.86
	15	1.98	2396833.35	2.06	2444290.65	4402794.64	14.96	44.48
	16	0.51	2371673.97	0.21	2383769.51	4471151.19	15.44	46.69
	17	1.74	2367222.82	0.17	2408412.50	4824156.59	14.04	50.08
	18	4.24	2293715.92	0.12	2390969.47	4761927.64	13.68	49.79
	19	0.81	2164985.48	0.14	2182521.86	4428984.09	13.78	50.72
	20	0.64	2198422.64	0.64	2212492.54	4654435.57	13.24	52.46
Eight hanging points & 1200 kg payload capacity	21	1.83	2232777.94	0.62	2273637.78	4942157.64	13.45	54.00
	22	0.49	2170507.96	0.31	2181143.45	4903109.18	13.52	55.52
	23	2.5	2133621.41	0.35	2186961.95	5173273.95	13.19	57.73
	24	2.16	2322472.15	0.44	2372637.55	4834518.65	13.49	50.92
	25	0.71	2169989.42	0.23	2185396.34	5112405.26	13.68	57.25
	26	1.12	2297950.78	0.52	2323687.83	5536982.21	13.31	58.03
	27	1.45	2210491.30	1.75	2242543.42	4830348.62	13.35	53.57
	28	0.27	2466338.72	0.34	2472997.83	5262662.30	13.33	53.01
	29	1.3	2318171.53	0.49	2348307.76	4953336.47	13.29	52.59
	30	0.61	2451781.82	0.28	2466737.69	5111780.87	14.08	51.74
Average		—	1.22	0.59	—	—	14.28	48.21

Table 3

Table 4

Performance characteristics of CPLEX and P-ALNS tested using 20 targets instances"

Armament	Number	Gap/%	CPLEX		P-ALNS
Armament	Number	Gap/%	Obj.Val.	Time/s	Obj.Val.	Initial solution	Time/s	Improvement/%
Four hanging points & 600 kg payload capacity	31	3.35	4899610.22	3343.28	5063697.54	7492363.51	172.83	32.42
	32	0.50	5034765.67^c	7200.00	5059977.76	7952110.85	181.88	36.37
	33	1.10	5520572.73	2101.08	5581197.55	8262242.54	174.34	32.45
	34	0.68	4782554.74^c	7200.00	4814935.01	7740820.10	159.98	37.80
	35	1.85	5126144.84	3342.66	5221043.14	8521157.63	181.46	38.73
	36	1.58	4983406.04	4139.49	5062217.82	7714785.85	177.81	34.38
	37	1.22	5426823.76	2861.28	5493187.76	8244558.53	202.44	33.37
	38	1.01	4768546.48	3295.02	4816711.65	7385545.18	159.35	34.78
	39	3.18	5082171.68	2296.35	5243736.55	8077279.46	214.01	35.08
	40	3.80	4893166.56	2864.81	5079180.63	8007398.00	175.60	36.57
Six hanging points & 900 kg payload capacity	41	0.42	6072122.90^c	7200.00	6097756.98	10133896.72	169.87	39.83
Six hanging points & 900 kg payload capacity	42	1.59	5169130.61	4139.63	5251331.52	9273408.90	331.76	43.37
Six hanging points & 900 kg payload capacity	43	3.06	6223474.21	4452.23	6413740.53	11126924.68	207.58	42.36
	44	1.09	5468838.52	2175.06	5528572.84	9717544.13	196.23	43.11
	45	3.66	5439860.50	3658.69	5638837.13	9352402.83	186.67	39.71
	46	2.45	5274467.03	3493.83	5403594.43	9749993.90	161.46	44.58
	47	0.89	5374977.48^c	7200.00	5422746.29	9191694.26	155.84	41.00
	48	1.86	6192449.97	2218.25	6307882.98	10540562.03	175.84	40.16
	49	2.29	6257508.70	4313.64	6400498.98	10622605.75	165.88	39.75
	50	0.99	6202262.45^c	7200.00	6263963.85	10172959.25	174.07	38.43
Eight hanging points & 1200 kg payload capacity	51	1.30	6308856.25	3009.60	6390566.83	12201417.94	160.00	47.62
	52	2.94	5480492.21	2080.21	5641612.05	11202576.31	155.18	49.64
	53	0.78	6070380.20^c	7200.00	6117562.61	12239885.06	164.25	50.02
	54	3.15	5522360.80	2815.72	5696050.07	11141702.42	157.08	48.88
	55	3.18	5533082.06	3096.89	5709138.24	9902122.84	177.69	42.34
	56	1.02	6512213.86	2760.13	6578654.03	11533613.36	152.47	42.96
	57	4.07	5833734.84	4166.18	6071186.17	11812193.19	163.27	48.60
	58	2.61	6443927.95	3942.82	6611908.41	11734979.34	159.43	43.66
	59	2.00	5933455.78	3558.42	6052242.16	11705717.35	168.15	48.30
	60	1.84	6010477.97	2740.77	6121263.88	11505725.93	162.27	46.80
Average		—	1.98	4002.20	—	—	178.16	41.10

Table 4

Table 5

Table 6

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Name		Index	Parameter value
UAV		Payload capacity of the UAV/kg	600, 900, 1200
		Number of hardpoints	4, 6, 8
		Type of weapons	W1, W2 and W3
		Cruise speed/(km/h)	180
Weapon	W1	Weight/kg	75
	W1	Cost/$ thousand	68
	W2	Weight/kg	165
	W2	Cost/$ thousand	84
	W3	Weight/kg	240
	W3	Cost/$ thousand	22

Instance size	Number of targets	Region/km²
Small	10 and 20	300 × 300
Medium	50	500 × 500
Large	100	800 × 800

Armament	Number	P-ALNS
Armament	Number	Obj.Val.	Initial solution	Time/s	Improvement/%
Four hanging points & 600 kg payload capacity	61	19639702.79	26418858.49	894.16	25.66
	62	20226587.53	28091423.00	905.93	28.00
	63	19075602.57	29497875.83	827.38	35.33
	64	19562878.93	28270698.47	885.11	30.80
	65	21225258.16	33479351.74	968.09	36.60
	66	21245526.49	30937990.54	903.73	31.33
	67	19025215.37	30255708.29	951.10	37.12
	68	18279376.77	25863988.21	934.00	29.32
	69	21500859.25	32517817.92	921.50	33.88
	70	19837561.10	27110162.60	843.02	26.83
Six hanging points & 900 kg payload capacity	71	23757260.36	38863692.78	864.34	38.87
	72	23626293.40	45297246.03	839.71	47.84
	73	25086301.95	43649491.24	897.37	42.53
Six hanging points & 900 kg payload capacity	74	23803380.80	40704101.47	957.38	41.52
	75	24953042.70	44968659.67	960.44	44.51
	76	20252252.48	33375639.30	951.77	39.32
	77	21484026.19	36297375.83	947.17	40.81
	78	20961277.39	34038993.24	962.00	38.42
	79	21290151.48	38375687.63	965.33	44.52
	80	23262131.65	43412205.51	871.07	46.42
Eight hanging points & 1200 kg payload capacity	81	26411759.24	51683908.96	916.99	48.90
	82	25927669.48	49991044.91	839.00	48.14
	83	28654411.71	64790006.23	925.85	55.77
	84	25173709.93	53936381.18	834.87	53.33
	85	24846209.02	51853298.48	943.74	52.08
	86	26687074.82	54588557.26	941.93	51.11
	87	26893376.17	59066981.34	843.04	54.47
	88	24835318.46	47791483.44	839.85	48.03
	89	29422705.85	62766002.15	849.10	53.12
	90	27632781.35	63730779.37	848.59	56.64
Average		—	—	901.12	42.04

Armament	Number	P-ALNS
Armament	Number	Obj.Val.	Initial solution	Time/s	Improvement/%
Four hanging points & 600 kg payload capacity	91	56713612.00	77833338.02	3658.66	27.13
	92	58819736.63	88335510.39	3939.26	33.41
	93	61499268.73	90584129.84	3620.54	32.11
	94	59363550.94	81817562.91	3480.32	27.44
	95	60015455.25	87037253.27	3427.54	31.05
	96	59031436.97	79440954.08	3691.88	25.69
	97	54676897.55	82662092.88	3331.97	33.85
	98	57308366.75	93759087.00	3532.63	38.88
	99	58273504.04	93494742.44	3337.27	37.67
	100	60034343.70	82882886.05	3913.52	27.57
Six hanging points & 900 kg payload capacity	101	74474010.78	120333006.01	3994.62	38.11
	102	70858242.62	129567870.06	3787.14	45.31
	103	63311816.71	117370935.19	3334.90	46.06
	104	63549955.83	105734877.09	3598.18	39.90
	105	80182413.87	134483508.76	3715.91	40.38
	106	60589994.73	101644904.13	3806.19	40.39
	107	74023035.54	123394701.99	3506.24	40.01
	108	72685374.15	139422975.33	3571.98	47.87
	109	68584173.73	131933825.44	3564.44	48.02
	110	70937380.62	127674074.42	3954.26	44.44
Eight hanging points & 1200 kg payload capacity	111	71011557.80	138470904.86	3455.29	48.72
Eight hanging points & 1200 kg payload capacity	112	79348300.75	188831368.78	3335.35	57.98
	113	87032222.17	174639693.52	3548.56	50.16
	114	78536295.79	155542205.60	3307.42	49.51
	115	79126976.77	169402740.94	3984.86	53.29
	116	81119272.13	162194167.94	3545.99	49.99
	117	80819186.75	191885864.20	3407.36	57.88
	118	89209526.06	177191089.51	3516.22	49.65
	119	78658940.98	166693102.07	3353.06	52.81
	120	84250485.60	163825165.08	3435.85	48.57
Average		—	—	3588.58	42.13

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Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

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