Weapon system portfolio selection based on structural robustness

doi:10.23919/JSEE.2020.000094

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (6): 1216-1229.doi: 10.23919/JSEE.2020.000094

• SYSTEMS ENGINEERING • Previous Articles Next Articles

Weapon system portfolio selection based on structural robustness

Jiuyao JIANG(), Jichao LI*(), Kewei YANG()

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

Received:2020-03-08 Online:2020-12-18 Published:2020-12-29
Contact: Jichao LI E-mail:jiangjy9@163.com;ljcnudt@hotmail.com;kayyan927@nudt.edu.cn
About author:|JIANG Jiuyao was born in 1998. She is a postgraduate student at National University of Defense Technology. Her research interest is modeling and evaluating equipment systems using the theory of complex systems. E-mail: jiangjy9@163.com||LI Jichao was born in 1990. He received his B.E. degree in management science M.E. and Ph.D. degrees in management science and engineering from National University of Defense Technology, Changsha, Hunan, China, in 2013, 2015, and 2019, respectively. He is currently a lecturer of management science and engineering at the National University of Defense Technology. Form 2017 to 2019, he was a visiting predoctoral fellow at the Northwestern Institute on Complex Systems (NICO) and Kellogg School of Management at Northwestern University, USA. His research interests focus on studying complex systems with a combination of theoretical tool and data analysis, including mathematical modeling of heterogeneous information networks, applying network methodologies to analyze the development of complex system-of-systems, and data-driven studying of the collective behavior of humans. E-mail: ljcnudt@hotmail.com||YANG Kewei was born in 1977. He received his B.E. and Ph.D. degrees in systems engineering, and management science and engineering from National University of Defense Technology, Changsha, Hunan, China, in 1999 and 2004, respectively. He is currently a professor of management science and engineering and the director of Department of Management, in the College of Information System and Management at National University of Defense Technology. He was a visiting scholar with the Department of Computer Science at the University of York in the United Kingdom, and with the Science and Technology on Complex Systems Simulation Laboratory, Beijing, China. His research interests focus on intelligent agent simulation, defense acquisition and system-of-systems requirement modeling. E-mail: kayyang27@nudt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (71690233; 71971213; 71571185) and Scientific Research Foundation of National University of Defense Technology (ZK19-16)

Abstract

Abstract:

The system portfolio selection is a fundamental frontier issue in the development planning and demonstration of weapon equipment. The scientific and reasonable development of the weapon system portfolio is of great significance for optimizing the design of equipment architecture, realizing effective resource allocation, and increasing the campaign effectiveness of integrated joint operations. From the perspective of system-of-systems, this paper proposes a unified framework called structure-oriented weapon system portfolio selection (SWSPS) to solve the weapon system portfolio selection problem based on structural invulnerability. First, the types of equipment and the relationship between the equipment are sorted out based on the operation loop theory, and a heterogeneous combat network model of the weapon equipment system is established by abstracting the equipment and their relationships into different types of nodes and edges respectively. Then, based on the combat network model, the operation loop comprehensive evaluation index (OLCEI) is introduced to quantitatively describe the structural robustness of the combat network. Next, a weapon system combination selection model is established with the goal of maximizing the operation loop comprehensive evaluation index within the constraints of capability requirements and budget limitations. Finally, our proposed SWSPS is demonstrated through a case study of an armored infantry battalion. The results show that our proposed SWSPS can achieve excellent performance in solving the weapon system portfolio selection problem, which yields many meaningful insights and guidance to the future equipment development planning.

Key words: heterogeneous combat network, structural robustness, weapon system portfolio selection, equipment development planning

Jiuyao JIANG, Jichao LI, Kewei YANG. Weapon system portfolio selection based on structural robustness[J]. Journal of Systems Engineering and Electronics, 2020, 31(6): 1216-1229.

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References 44

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Node type	S	D	I	T
S	$ S\to S $	$ S\to D $	$ S\to I $	$ S\to T $
D	$ D\to S $	$ D\to D $	$ D\to I $	$ D\to T $
I	$ I\to S $	$ I\to D $	$ I\to I $	$ I\to T $
T	$ T\to S $	$ T\to D $	$ T\to I $	$ T\to T $

Edge type	Meaning
$ T\to S $	Scout equipment detects enemy targets.
$ S\to S $	Information shares between two scout equipment.
$ S\to D $	The scout equipment uploads the detected intelligence to the decision equipment.
$ D\to S $	The decision equipment issues instructions to the scout equipment.
$ D\to D $	Information shares between the two decision devices or one part gives instructions to the other.
$ D\to I $	The decision equipment issues orders to the influence equipment.
$ I\to T $	The influence equipment fires or interferes with enemy targets.

Equipment	Quantity	Classification of meta-function nodes
Equipment	Quantity	Scout	Decision	Influence
Armored command vehicle	1	√	√	√
Command tank	1	√	√	√
Main battle tank	12	√	√	√
Infantry fighting vehicle	6	√	√	√
Armored frontier observation command vehicle	1	√	√
Self-propelled howitzer	4			√
Low-altitude search and warning radar vehicle	1	√
Self-propelled artillery	4			√
Armored reconnaissance vehicle	2	√
Drone	1	√
Reconnaissance intelligence processing vehicle	2	√	√
Reconnaissance attack helicopter	1	√		√

Equipment	Quantity	Sign	Scout capability	Decision capability	Impact capability	Cost
Armored command vehicle	1	A	4	7	5	10
Self-propelled howitzer	2	B	—	—	7	5
Self-propelled artillery	2	C	—	—	5	5
Armored reconnaissance vehicle	1	D	7	—	5	7
Drone	1	E	9	—	—	6
Reconnaissance intelligence processing vehicle	1	F	8	6	—	7
Reconnaissance attack helicopter	2	G	7	—	8	9

Sign	Algebraic connectivity		Network efficiency		Network structure entropy		OLCEI
Sign	Value	Cost-benefit ratio	Value	Cost-benefit ratio	Value	Cost-benefit ratio	Value	Cost-benefit ratio
1	1.033 2	0.024 6	0.524 2	0.012 481	3.514 2	0.083 671	15.133 34	0.360 318
2	1.033 4	0.024 605	0.523 8	0.012 471	3.511 4	0.083 605	15.130 09	0.360 24
3	1.034 8	0.023 518	0.535 4	0.012 168	3.521 7	0.080 039	15.938 09	0.362 229
4	1.030 1	0.023 956	0.513 6	0.011 944	3.539 3	0.082 309	15.158 08	0.352 513
5	1.031 3	0.022 918	0.524 3	0.011 651	3.546 2	0.078 804	15.960 87	0.354 686
6	1.034 2	0.025 855	0.613 3	0.015 333	3.589 1	0.089 728	15.256 94	0.381 423
7	1.032 6	0.022 947	0.579 2	0.012 871	3.570 1	0.079 336	15.270 75	0.339 35
8	1.032 5	0.022 944	0.578 9	0.012 864	3.567 4	0.079 276	15.267 48	0.339 277
9	1.034 3	0.025 858	0.614 1	0.015 353	3.587 3	0.089 683	15.253 65	0.381 341
10	1.032 8	0.022 951	0.578 5	0.012 856	3.564 5	0.079 211	15.264 21	0.339 205
11	1.035 7	0.024 660	0.626 2	0.014 910	3.613 3	0.086 031	16.060 74	0.382 399
12	1.032 9	0.024 021	0.576 5	0.013 407	3.572 1	0.083 072	14.960 06	0.347 908
13	1.032 8	0.024 019	0.576 2	0.013 4	3.569 2	0.083 005	14.957 77	0.347 855
14	1.033 2	0.024 028	0.575 8	0.013 391	3.566	0.082 93	14.955 49	0.347 802

Weapon system portfolio selection based on structural robustness

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Sign	Equipment portfolio	Scout capability	Decision capability	Impact capability	Cost	Structural robustness	Cost-benefit ratio
1	A, B, C, E, F, G	28	13	25	42	15.133 34	0.360 318
2	A, B, B, E, F, G	28	13	27	42	15.130 09	0.360 24
3	A, B, D, E, F, G	35	13	25	44	15.938 09	0.362 229
4	A, B, B, C, C, E, F	21	13	29	43	15.158 08	0.352 513
5	A, B, B, C, D, E, F	35	13	29	45	15.960 87	0.354 686
6	A, C, F, G, G	26	13	26	40	15.256 94	0.381 423
7	A, C, C, F, G, G	26	13	31	45	15.270 75	0.339 35
8	A, B, C, F, G, G	26	13	33	45	15.267 48	0.339 277
9	A, B, F, G, G	26	13	28	40	15.253 65	0.381 341
10	A, B, B, F, G, G	26	13	35	45	15.264 21	0.339 205
11	*A, D, F, G, G*	33	13	26	42	16.060 74	0.382 399
12	A, C, C, D, F, G	26	13	28	43	14.960 06	0.347 908
13	A, B, C, D, F, G	26	13	30	43	14.957 77	0.347 855
14	A, B, B, D, F, G	26	13	32	43	14.955 49	0.347 802