Risk transmission evaluation for parallel construction of warships based on IFCM and the cloud model

doi:10.23919/JSEE.2022.000144

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (6): 1224-1237.doi: 10.23919/JSEE.2022.000144

• SYSTEMS ENGINEERING • Previous Articles

Risk transmission evaluation for parallel construction of warships based on IFCM and the cloud model

Jun GONG(), Tao HU(), Lu YAO

¹ Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan 430033, China

Received:2021-01-31 Online:2022-12-18 Published:2022-12-24
Contact: Jun GONG E-mail:47224843@qq.com;hutao188@sohu.com
About author:
GONG Jun was born in 1982. He is a Ph.D. and a lecturer at the Department of Management Engineering and Equipment Economics, Naval University of Engineering. As a project leader, he presided over two key naval projects, and more than 10 scientific research projects. In recent years, he published nearly 30 academic papers in core journals, one monograph and five teaching materials, and was selected as the middle-aged and young discipline leader of Naval University of Engineering. His research interests are complex system modeling and simulation, equipment quality management, system management and others. E-mail: 47224843@qq.com

HU Tao was born in 1970. He is a Ph.D. and a professor at the Department of Management Engineering and Equipment Economics, Naval University of Engineering. As the project leader, he has presided over more than 30 military level scientific research projects, published more than 70 academic papers in core journals, and more than 10 monographs, and won two third prizes for military scientific and technological progress. His research interests are the research and application of equipment management, system management and other fields. E-mail: hutao188@sohu.com

YAO Lu was born in 1979. He received his Ph.D. degree from Huazhong University of Science and Technology. He is an associate professor at the Department of Management Engineering and Equipment Economics, Naval University of Engineering. As a project leader, he has presided over more than 20 military level scientific research projects, and published more than 30 academic papers in core journals. His research interests include information ma-nagement, and system management. E-mail: yaoluV@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (71501183).

Abstract

Abstract:

To cope with multi-directional transmission coupling, spreading, amplification, and chain reaction of risks during multi-project parallel construction of warships, a risk transmission evaluation method is proposed, which integrates an intuitionistic cloud model with a fuzzy cognitive map. By virtue of expectancy $ {\rm{Ex}} $ , entropy ${\rm{En}}$ , and hyper entropy ${\rm{He}}$ , the risk fuzziness and randomness of the knowledge of experts are organically combined to develop a method for converting bi-linguistic variable decision-making information into the quantitative information of the intuitionistic normal cloud (INC) model. Subsequently, the threshold function and weighted summation operation in the traditional fuzzy cognitive map is converted into the INC ordered weighted averaging operator to create the risk transmission model based on the intuitionistic fuzzy cognitive map (IFCM) and the algorithm for solving it. Subsequently, the risk influence sequencing method based on INC and the risk rating method based on nearness are proposed on the basis of Monte Carlo simulation in order to realize the mutual conversion of the qualitative and quantitative information in the risk evaluation results. Example analysis is presented to verify the effectiveness and practicality of the methods.

Key words: risk evaluation, multi-project parallel, intuitionistic fuzzy cognitive map (IFCM), intuitionistic normal cloud (INC) model, warship construction

Jun GONG, Tao HU, Lu YAO. Risk transmission evaluation for parallel construction of warships based on IFCM and the cloud model[J]. Journal of Systems Engineering and Electronics, 2022, 33(6): 1224-1237.

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References 24

1	PRITSKER A B, WATTERS L J, WOLFE P M Multiproject scheduling with limited resource: a zero-one programming approach. Management Science, 1996, 16 (1): 93- 108.
2	LI C B, LIU Y Q. Multi-project risk element transfer theory and its application. Beijing: China Electric Power Press, 2015. (in Chinese)
3	LI C B, MA T T. Smart grid operation risk element transfer theory and its application. Beijing: China Electric Power Press, 2016. (in Chinese)
4	LIU Z Q. Research on project chain risk elements feedback transmission and its information system in construction project. Beijing: North China Electric Power University, 2016. (in Chinese)
5	LI C B, FENG X, QI Z Q Research on risk multi-criteria decision-making under the linguistic environment. Statistics & Decision, 2015, 12, 34- 38.
6	CHEN H Y. Research on risk management models and applications for electric power generation EPC project. Beijing: North China Electric Power University, 2017. (in Chinese)
7	LI S K. The research on model of risk elements transmission and optimizing of microgrids in context of energy big data. Beijing: North China Electric Power University, 2017. (in Chinese)
8	SUN Y, WANG Y, LI C Complex equipment risk conduction analysis based on UR-MTPGERT model. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44 (8): 1587- 1595.
9	WANG Y, SUN Y, MENG X F, et al Research on risk transfer GERT of complex equipment systems based on opportunity theory. Systems Engineering and Electronics, 2018, 40 (12): 2707- 2713.
10	LI C, WANG Y, CHEN C, et al GERT analysis of coupled conduction for materiel hazard based on QHSME. Systems Engineering and Electronics, 2014, 36 (11): 2219- 2225.
11	TAO Q, LI C, LI Z X, et al GERT-MC based analysis of transmission of failure risks in aircraft main landing gear system. China Safety Science Journal, 2018, 28 (3): 108- 113.
12	SUN C, WANG Y, ZHANG L, et al Simulation research on maintenance men ’s risk transmission from perspective of complex network. Application Research of Computers, 2017, 34 (4): 1093- 1096.
13	BAI Y, ZHANG Z F Risk conduction assessment of weapon development projects by using fuzzy cloud model. Journal of Harbin Institute of Technology, 2016, 48 (10): 168- 175.
14	YANG M, ZHOU S H, WEI F J, et al. Analysis on quality risk transmission of complex materiel development. Beijing: Science Press, 2017. (in Chinese)
15	YANG E E. Duplex linguistic MCDM methods. Beijing: Intellectual Property Publishing House, 2016. (in Chinese)
16	MICHAEL G Fuzzy cognitive strategic maps in business process performance measurement. Expert Systems with Applications, 2013, 40 (1): 1- 14. doi: 10.1016/j.eswa.2012.01.078
17	LI H, CHEN H Q, MA L Y, et al A review of algorithm improvement and application of fuzzy cognitive map. Journal of Nanjing University (Natural Sciences), 2016, 52 (4): 746- 761.
18	WANG J Q, YANG E E Multiple criteria group decision making method based on intuitionistic normal cloud by Monte Carlo simulation. Systems Engineering Theory & Practice, 2013, 33 (11): 2859- 2865.
19	LI C, DUANMU J S, LEI Y J, et al Situation assessment based on cognitive maps and intuitionistic fuzzy reasoning. Systems Engineering and Electronics, 2012, 34 (10): 2064- 2068.
20	WANG X F. Research on intuitionistic linguistic multiple criteria decision-making methods. Beijing: Intellectual Property Publishing House, 2016. (in Chinese)
21	WANG Y N, LEI Y J, LEI Y, et al Multi-factor high-order intuitionistic fuzzy times series forecasting model. Journal of Systems Engineering and Electronics, 2016, 27 (5): 1054- 1062.
22	MALIKA S, WANG F Y, LIU Z X, et al Distributed fuzzy fault-tolerant consensus of leader-follower multi-agent systems with mismatched uncertainties. Journal of Systems Engineering and Electronics, 2021, 32 (5): 1031- 1040.
23	ZHAO S, MAKIS V, CHEN S W, et al Health evaluation method for degrading systems subject to dependent competing risks. Journal of Systems Engineering and Electronics, 2018, 29 (2): 436- 444.
24	STYLIOS C D, GROUMPOS P P Fuzzy cognitive maps: a model for intelligent supervisory control systems. Computers in Industry, 1999, 39 (3): 229- 238. doi: 10.1016/S0166-3615(98)00139-0

Operation	$ \lambda {Y_1} $	$ {Y_1} \oplus {Y_2} $	$ {Y_1} \otimes {Y_2} $
${\rm{Ex}}$	$\lambda {{\rm{Ex}}_1}$	${\rm{Ex}}{_1} + {\rm{Ex}}{_2}$	${\rm{Ex}}{_1}{\rm{Ex}}{_2}$
$ \rho $	$ {\rho _1} $	$\dfrac{ { {\rho _1}{ {\rm{Ex} }_1} + {\rho _2}{ {\rm{Ex} }_2} } }{ { { {\rm{Ex} }_1} + {{\rm{Ex}}_2} } }$	$ {\rho _1}{\rho _2} $
$ \nu $	$ {\nu _1} $	$\dfrac{ { {\nu _1}\rm{Ex}{_1} + {\nu _2}{\rm{Ex}_2} } }{ {{\rm{Ex}_1} + \rm{Ex}{_2} } }$	$ {\nu _1} + {\nu _2} - {\nu _1}{\nu _2} $
${\rm{En}}$	$\lambda {{\rm{En}}_1}$	$\sqrt {{{\rm{En}}_1}^2 + {{\rm{En}}_2}^2}$	$\left\| {{{\rm{Ex}}_1}{{\rm{Ex}}_2} } \right\| \times \sqrt { { {\left( {\dfrac{ {{{\rm{En}}_1} } }{ {{{\rm{Ex}}_1} } } } \right)}^2} + { {\left( {\dfrac{ {{{\rm{En}}_2} } }{ {{{\rm{Ex}}_2} } } } \right)}^2} }$
${\rm{He}}$	$\lambda {{\rm{He}}_1}$	$\sqrt {{{\rm{He}}_1}^2 + {{\rm{He}}_2}^2}$	$\left\| { { {\rm{Ex} }_1}{{\rm{Ex}}_2} } \right\| \times \sqrt { { {\left( {\dfrac{ { { {\rm{He} }_1} } }{ { { {\rm{Ex} }_1} } } } \right)}^2} + { {\left( {\dfrac{ { { {\rm{He} }_2} } }{ { { {\rm{Ex} }_2} } } } \right)}^2} }$

Item	Risk event
1	$ A_1^1,A_2^1,A_3^1,A_4^1,A_5^1,A_6^1,A_8^1 $
2	$ A_1^2 ,A_2^2 ,A_3^2,A_4^2,A_6^2,A_9^2$
3	$ A_1^3,A_2^3,A_4^3,A_5^3,A_6^3,A_7^3$
4	$ A_1^4,A_3^4,A_4^4,A_5^4,A_6^4$

Level	Occurrence probability/%	Linguistic scale	Representation in normal cloud model
1	<10	$ {s_1} $	$ (0,0.104,0.026) $
2	10−30	$ {s_2} $	$ (0.309,0.064,0.016) $
3	30−70	$ {s_3} $	$ (0.5,0.040,0.010) $
4	70−90	$ {s_4} $	$ (0.691,0.064,0.016) $
5	>90	$ {s_5} $	$ (1,0.104,0.026) $

Linguistic membership	Level of confidence in evaluation results	$ h \to \left( {\rho ,\nu } \right) $
$ {h_1} $	Very low	$ (0,0.333) $
$ {h_2} $	Low	$ (0.167,0.5) $
$ {h_3} $	Middle	$ (0.333,0.667) $
$ {h_4} $	High	$ (0.5,0.833) $
$ {h_5} $	Very high	$ (0.667,1) $

Risk level	Linguistic information of qualitative level	$Y_l^{'}$	${\hat z_l}$
1	Very low	$ \left( {\left\langle {0,0.667,1} \right\rangle ,0.104,0.026} \right) $	0
2	Low	$ \left( {\left\langle {0.309,0.667,1} \right\rangle ,0.064,0.016} \right) $	0.060
3	Middle	$ \left( {\left\langle {0.5,0.667,1} \right\rangle ,0.040,0.010} \right) $	0.096
4	High	$ \left( {\left\langle {0.691,0.667,1} \right\rangle ,0.064,0.016} \right) $	0.133
5	Very high	$ \left( {\left\langle {1,0.667,1} \right\rangle ,0.104,0.026} \right) $	0.192

Risk transmission evaluation for parallel construction of warships based on IFCM and the cloud model

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Risk influence	Weighted ${ {\hat{z} }_{l} } $	Risk level	Risk influence	${ {\hat{z} }_{l} } $	Risk level
$ {R_1} $	0.146	4	$ {R_{1Q}} $	0.175	5
			$ {R_{1S}} $	0.157	4
			$ {R_{1P}} $	0.064	2
$ {R_2} $	0.031	2	$ {R_{2Q}} $	0.058	2
			$ {R_{2S}} $	0.009	1
			$ {R_{2P}} $	0.036	2
$ {R_3} $	0.150	4	$ {R_{3Q}} $	0.132	4
			$ {R_{3S}} $	0.163	5
			$ {R_{3P}} $	0.161	4
$ {R_4} $	0.112	3	$ {R_{4Q}} $	0.130	1
			$ {R_{4S}} $	0.156	2
			$ {R_{4P}} $	0.094	3
$ {R_5} $	0.023	1	$ {R_{5Q}} $	0.094	3
			$ {R_{5S}} $	0.021	1
			$ {R_{5P}} $	0.019	1
$ {R_6} $	0.105	3	$ {R_{6Q}} $	0.122	4
			$ {R_{6S}} $	0.097	3
			$ {R_{6P}} $	0.091	3
$ {R_7} $	0.034	2	$ {R_{7Q}} $	0.031	2
			$ {R_{7S}} $	0.044	2
			$ {R_{7P}} $	0.019	1
$ {R_8} $	0.073	2	$ {R_{8Q}} $	0.096	3
			$ {R_{8S}} $	0.068	2
			$ {R_{8P}} $	0.038	2
$ {R_9} $	0.026	1	$ {R_{9Q}} $	0.029	1
			$ {R_{9S}} $	0.042	2
			$ {R_{9P}} $	0.009	1
$ R $	0.096	3	—	—	—

Cloud droplets parameter	Simulation value	Cloud droplets parameter	Simulation value
Sample size	10000	Skewness	0.098
Average	0.096	Peakness	1.49
Median	0.097	Coefficient of variation	0.513
Standard deviation	1.2	Minimum	0
Variance	2.15	Maximum	0.188

Time	$ {\bar z_1} $
1	0.0956
2	0.0937
3	0.0942
4	0.0937
5	0.0953
6	0.0955
7	0.0937
8	0.0945
9	0.0934
10	0.0950
11	0.0946
12	0.0949
13	0.0929
14	0.0947
15	0.0959
16	0.0949
17	0.0953
18	0.0930
19	0.0939
20	0.0943
Average	0.095
Variance	0.0001

Risk influence	Considering linguistic membership		Without considering linguistic membership
Risk influence	$ {\hat z_l} $	Risk level	$ {\hat z_l}{'} $	Risk level
$ {R_1} $	0.146	4	0.158	4
$ {R_2} $	0.031	2	0.039	2
$ {R_3} $	0.150	4	0.165	5
$R_4$	0.112	3	0.119	4
$R_5$	0.023	1	0.041	2
$R_6$	0.105	3	0.109	3
$R_7$	0.034	2	0.042	2
$R_8$	0.073	2	0.084	3
$R_9$	0.026	1	0.031	2
$R$	0.096	3	0.103	3

Risk influence	The proposed method		Method 1
Risk influence	$ {\hat z_l} $	Risk level	$ {\hat z_l}{'} $	Risk level
$ {R_1} $	0.146	4	0.112	3
$ {R_2} $	0.031	2	0.025	1
$ {R_3} $	0.150	4	0.145	4
${R_4}$	0.112	3	0.095	3
${R_5}$	0.023	1	0.020	1
${R_6}$	0.105	3	0.099	3
${R_7}$	0.034	2	0.029	1
${R_8}$	0.073	2	0.074	2
${R_9}$	0.026	1	0.021	1
${R}$	0.096	3	0.077	2