High-end equipment development task decomposition and scheme selection method

doi:10.23919/JSEE.2021.000012

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (1): 118-135.doi: 10.23919/JSEE.2021.000012

• SYSTEMS ENGINEERING • Previous Articles Next Articles

High-end equipment development task decomposition and scheme selection method

Xiangqian XU(), Kewei YANG, Yajie DOU*(), Zhexuan ZHOU(), Ziyi CHEN(), Yuejin TAN

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

Received:2020-04-27 Online:2021-02-25 Published:2021-02-25
Contact: Yajie DOU E-mail:xuxiangqian18@163.com;yajiedou_nudt@163.com;zhouzhexuan16@163.com;chenziyi_nudt@163.com
About author:|XU Xiangqian was born in 1995. He is a Ph.D. student in National University of Defense Technology. His research interests are defense acquisition, system project management, and data driven decision. E-mail: xuxiangqian18@163.com||YANG Keweiwas born in 1977. He received his B.E. degree in systems engineering and Ph.D. degree in management science and engineering from National University of Defense Technology, Changsha, Hunan, China, in 1999 and 2004, respectively. He is currently a professor and the director of the Department of Management Science and Engineering, College of Systems Engineering, 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. His current research interests include intelligent agent simulation, defense acquisition, and system-of-systems requirement modeling. E-mail: kayyang27@nudt.edu.cn||DOU Yajie was born in 1987. He is a Ph.D. and an associate professor in National University of Defense Technology. His research interests are weapon alternative decision and effectiveness evaluation, system-of-systems architecting and engineering management, and portfolio decision analysis. E-mail: yajiedou_nudt@163.com||ZHOU Zhexuan was born in 1994. He is a graduate student in National University of Defense Technology. His research interests are systems engineering, portfolio selection analysis, groupdecision-making, and fuzzy decision-making. E-mail: zhouzhexuan16@163.com||CHEN Ziyi was born in 1995. He is a Ph.D. student in National University of Defense Technology. His research interests are complex systems and system portfolio selection and optimization. E-mail: chenziyi_nudt@163.com||TAN Yuejin was born in 1958. He is a professor in National University of Defense Technology. His research interests are system of systems (SoS) requirements modeling, SoS architecture design and optimization, complex network, and system modeling and simulation. E-mail: yjtian@nudt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (71690233; 71901214), and the National Key R&D Program of China (2017YFC1405005)

Abstract

Abstract:

Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes. However, such procedures are highly innovative, technology intensive, interdisciplinary, and multi-party engineering projects, making the decomposition and scheme selection more difficult and complicated than that in the development of ordinary equipment. In this study, we consider three factors, namely, functional structure, task granularity, and task feasibility in task decomposition of high-end equipment development. Based on the principles of systems engineering, a method of task decomposition is proposed. As for decomposition scheme selection, a method based on the superiority and inferiority ranking (SIR) method mixed information and multiple attribute decision making is proposed by considering attributes of scheme feasibility, uncertainty risk and task integration complexity. To verify the proposed method, development of a military electric vehicle is used as an example to demonstrate the calculation process.

Key words: high-end equipment, task decomposition, decomposition scheme selection

Xiangqian XU, Kewei YANG, Yajie DOU, Zhexuan ZHOU, Ziyi CHEN, Yuejin TAN. High-end equipment development task decomposition and scheme selection method[J]. Journal of Systems Engineering and Electronics, 2021, 32(1): 118-135.

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References 28

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Ranking method	Score value	Sequence
Xia and Xu[22]	$S\left( {{h_1}} \right) = S\left( {{h_2}} \right) = S\left( {{h_3}} \right) = 0.4$	${h_1} \sim {h_2} \sim {h_3}$
Farhadinia[23]	$S\left( {{h_1}} \right) = 0.467$ ${S\left( {{h_2}} \right) = 0.4}$ ${S\left( {{h_3}} \right) = 0.467}$	${{h_1}\sim{h_3} \succ {h_2}}$
Liao and Xu [25]	${S\left( {{h_1}} \right) = S\left( {{h_2}} \right) = S\left( {{h_3}} \right) = 0.4}$ ${v\left( {{h_1}} \right) = 0.1}$ ${v\left( {{h_2}} \right) = 0}$ ${v\left( { {h_3} } \right) = 0.163\;3}$	${{h_2} \succ {h_1} \succ {h_3}}$
Zhang and Xu ( $\delta = 0.1$ )[24]	${S\left( { {h_1} } \right) = 0.388\;6}$ ${S\left( {{h_2}} \right) = 0.4}$ ${S\left( { {h_3} } \right) = 0.367\;2}$	${h_2} \succ {h_1} \succ {h_3}$
Our study	${S\left( { {h_1} } \right) = 0.387\;3}$ ${S\left( {{h_2}} \right) = 0.4}$ ${S\left( { {h_3} } \right) = 0.363\;4}$	${h_2} \succ {h_1} \succ {h_3}$

(i) Common criterion	(ii) Pseudo criterion	(iii) Indifference interval linear preference criterion
${\rm{g} }(p) = \left\{ {\begin{array}{*{20}{c} } {\!\!\!\!\!1,\;\;p > 0} \\ {\!\!\!\!\!0,\;\;p \leqslant 0} \end{array} } \right.$	${\rm{g} }(p) = \left\{ {\begin{array}{*{20}{c} } {\!\!\!\!\!1,\;\;p > {t_2} } \\ {\!\!\!\!\!0,\;\;p \leqslant {t_2} } \end{array} } \right.$	${\rm{g} }(p) = \left\{ \begin{array}{l}\!\!\!\!\!{\rm{1,} }\;\;p > {t_1}\\\!\!\!\!\!\dfrac{ {d - q} }{ {p - q} },\;{t_2} < p \leqslant {t_1}\\\!\!\!\!\!0,\;\;p \leqslant {t_2}\end{array} \right.$

(ⅳ) Grading criterion	(ⅴ) Linear preference criterion	(ⅵ) Gauss criterion
${\rm{g} }(p) = \left\{ \begin{array}{l}\!\!\!\!\!{\rm{1,} }\;\;p > {t_1}\\\!\!\!\!\!\dfrac{1}{2},{t_2} < {\rm{ } }p \leqslant{t_1}{\rm{ } }\\\!\!\!\!\!0,\;\;p \leqslant {t_2}\end{array} \right.$	${\rm{g} }(p) = \left\{ \begin{array}{l}\!\!\!\!\!{\rm{1,} }\;\;p > {t_1}\\\!\!\!\!\!\dfrac{p}{ { {t_1} } },\;0 < p \leqslant {t_1}\\\!\!\!\!\!0,\;\;p \leqslant 0\end{array} \right.$	${\rm{g} }(p) = \left\{ \begin{array}{l}\!\!\!\!\!1 - \exp \left( {\dfrac{ { - {d^2} } }{ {2{\sigma ^2} } } } \right),\;\;p > 0\\\!\!\!\!\!0,\;\;p \leqslant 0\end{array} \right.$

Serial number	Project R&D name	Serial number	Project R&D name
1	Electric drive system	17	Vehicle controller
2	Power system	18	Motor controller
3	Power battery	19	Current sensor
4	Battery management system	20	Voltage sensor
5	Car charger	21	Temperature sensor
6	Auxiliary power source	22	Body
7	Drive motor system	23	Body-in-white
8	Electronic controller	24	Body safety guard
9	Power converter	25	Auxiliary system
10	Drive motor	26	Automotive instrumentation, lighting and accessories
11	Mechanical transmission	27	Power steering
12	Clutch	28	Steering mechanism
13	Transmission	29	Steering gear
14	Transmission shaft and other universal transmissions	30	Steering transmission mechanism
15	Axle (main reducer, differential axle housing, etc.)	31	Front and rear suspension
16	Wheels	32	Braking system

Task number	Task correlation coefficient	Task reused coefficient	Task granularity coefficient
A	0.25	0.187 5	0.047
B	0	0	0
C	0	0	0
D	0.5	0.125	0.062 5

Task number	Sub-task set	Project R&D activity	Task number	Sub-task set	Project R&D activity
Scheme 1	A1	2,3,4,5,6	Scheme 3	A	1,2,3,4,5,6,7, 8,9,10,11,12,13,14,15,16
	A2	7,8,9,10,11,12,13,14,15,16		B	17,18,19,20,21
	A3	1,2,7		C	22,23,24
	B	17,18,19,20,21		D1	25,26,27,31,32
	C	22,23,24		D2	28,29,30,31,32
	D	25,26,27,28,29,30,31,32
Scheme 2	A1	2,3,4,5,6	Scheme 4	A1	2,3,4,5,6
	A21	7,8,9,10,11,16		A2	7,8,9,10,11,12,13,14,15,16
	A22	11,12,13,14,15		A3	1,2,7
	A3	1,2,7		B	17,18,19,20,21
	B	17,18,19,20,21		C	22,23,24
	C	22,23,24		D1	25,26,27,31,32
	D	25,26,27,28,29,30,31,32		D2	28,29,30,31,32

High-end equipment development task decomposition and scheme selection method

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Task number	Task granularity
Scheme 1	0.01875
Scheme 2	0.00890
Scheme 3	0.00938
Scheme 4	0.00670

Sub-task	Number of suppliers	Sub-task	Number of suppliers
A	40	B	80
A1	80	C	80
A22	80	D	80
A3	80	D1	90
A4	100	D2	100

Task number	Solution feasibility
Scheme 1	0.8000
Scheme 2	0.8286
Scheme 3	0.7800
Scheme 4	0.8429

Scheme number	Evaluation value
Scheme 1	(0.6, 0.65)
Scheme 2	(0.4, 0.4, 0.45)
Scheme 3	(0.65, 0.7)
Scheme 4	(0.55, 0.6)

Scheme number	Uncertain risk
Scheme 1	0.6162
Scheme 2	0.4160
Scheme 3	0.6630
Scheme 4	0.5662

Scheme number	Uncertain risk score value
Scheme 1	0.3838
Scheme 2	0.5840
Scheme 3	0.3370
Scheme 4	0.4338

Scheme number	Task level	Number of sub-tasks
Scheme 1	2	6
Scheme 2	3	7
Scheme 3	2	5
Scheme 4	2	7

Scheme number	Task integration complexity
Scheme 1	0.9559
Scheme 2	0.9859
Scheme 3	0.9473
Scheme 4	0.9621

Scheme number	Task integration complexity score value
Scheme 1	0.0441
Scheme 2	0.0141
Scheme 3	0.0527
Scheme 4	0.0379

Scheme number	Advantage flow	Disadvantage flow	Net flow	Ranking
Scheme 1	0.000300	0.0032	?0.0029	3
Scheme 2	0.004800	0.0001	0.0047	1
Scheme 3	0.000100	0.0049	?0.0048	4
Scheme 4	0.001100	0.0017	?0.0006	2

Algorithm	Steps required for decomposition	Gap with min/%
The proposed method	24	—
Minimum tree decomposition	56	133.3
Minimum functional module decomposition	30	25
Heuristic deep decomposition	25	4.2
Minimum value	24	—

[1]	Zhigang Zou, Wangfang Che, Shusheng Wang, Yun Bai, and Chengli Fan. Role-based approaches for operational tasks modeling and flexible decomposition [J]. Journal of Systems Engineering and Electronics, 2016, 27(6): 1191-1206.
[2]	Aijun Liu, Michele Pfund, and John Fowler. Scheduling optimization of task allocation in integrated manufacturing system based on task decomposition [J]. Journal of Systems Engineering and Electronics, 2016, 27(2): 422-433.

Algorithm	Scheme sorting result	Score value of each scheme	Deviation between the score and the mean of each scheme
The proposed method	Scheme 2 > Scheme 4 > Scheme 1 > Scheme 3	Scheme 1: 2 Scheme 2: 4 Scheme 3: 1 Scheme 4: 3	Scheme 1: 0% Scheme 2: 0% Scheme 3: 20% Scheme 4: 9.1%
TOPSIS	Scheme 2 > Scheme 4 > Scheme 1 > Scheme 3	Scheme 1: 2 Scheme 2: 4 Scheme 3: 1 Scheme 4: 3	Scheme 1: 0% Scheme 2: 0% Scheme 3: 20% Scheme 4: 9.1%
AHP	Scheme 2 > Scheme 1 > Scheme 4 > Scheme 3	Scheme 1: 3 Scheme 2: 4 Scheme 3: 1 Scheme 4: 2	Scheme 1: 50% Scheme 2: 0% Scheme 3: 20% Scheme 4: 27.3%
ELECTRE	Scheme 2 > Scheme 4 > Scheme 3 > Scheme 1	Scheme 1: 1 Scheme 2: 4 Scheme 3: 2 Scheme 4: 3	Scheme 1: 50% Scheme 2: 0% Scheme 3: 60% Scheme 4: 9.1%
Average	Scheme 2 > Scheme 4 > Scheme 1 > Scheme 3	Scheme 1: 2 Scheme 2: 4 Scheme 3: 1.25 Scheme 4: 2.75	—