Mission-oriented dynamic optimization model of carrying spares for warship redundant system

doi:10.21629/JSEE.2018.03.11

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (3): 539-548.doi: 10.21629/JSEE.2018.03.11

• Systems Engineering • Previous Articles Next Articles

Mission-oriented dynamic optimization model of carrying spares for warship redundant system

Minzhi RUAN¹(), Hua LI^2,*(), Junlong WANG¹()

¹ College of Naval Architecture & Marine Engineering, Naval University of Engineering, Wuhan 430033, China
² College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China

Received:2016-12-26 Online:2018-06-28 Published:2018-07-02
Contact: Hua LI E-mail:ruanminzhi830917@sina.com;23452390@qq.com;daibanzhu123@163.com
About author:RUAN Minzhi was born in 1983. He received his B.S. degree and M.S. degree in 2006 and 2009 respectively in military command from Dalian Naval Academy, and Ph.D. degree in weapon system and application engineering from Naval University of Engineering in 2012. Now he works at College of Naval Architecture & Marine Engineering, Naval University of Engineering. His main research interests are naval warship equipment logistic support, support resource optimization, equipment support modeling and simulation. E-mail: ruanminzhi830917@sina.com|LI Hua was born in 1972. He received his Ph.D. degree in weapon system and application engineering from Naval University of Engineering in 2007. Currently, he is an associate professor at College of Weaponry Engineering, Naval University of Engineering. His main research interests are equipment logistic support and naval weaponry simulation technology. E-mail: 23452390@qq.com|WANG Junlong was born in 1985. He received his B.S. degree in computer information management from Naval University of Engineering in 2009, and M.S. degree in computer application technology from Wuhan Textile University in 2012. Now he works at College of Naval Architecture & Marine Engineering, Naval University of Engineering. His main research interests are information technology of equipment support and support resource optimization. E-mail: daibanzhu123@163.com
Supported by:
the National Defense Pre-research Project in the 13th Five-Year(41404050502);the National Defense Science and Technology Fund of the Central Military Commission(2101140);This work was supported by the National Defense Pre-research Project in the 13th Five-Year (41404050502) and the National Defense Science and Technology Fund of the Central Military Commission (2101140)

Abstract

Abstract:

Redundancy is a common structure for warship system, and it is an effective way to improve the reliability of the system. In this paper, warship system is taken as the object of study, based on the system reliability equivalence principle, a spares demand rate calculation method for redundant system is proposed through structure transformation. According to the system analysis method, the general modeling data structure of spares support echelon and system indenture is established, and the mission success probability is taken as the optimization target to build the dynamic optimization model of carrying spares during the process of multi-phase. By introducing the Lagrange multiplier, the spares weight, volume and cost are transformed to the single target of the spares total scale, and the initial Lagrange factors and its dynamic adjustment policy is proposed. In a given example, the main influence factors of the carrying spares project are analyzed, and the study results are in accordance with the reality, which can provide a new approach to mission-oriented carrying spares optimization for the redundant system.

Key words: redundancy, reliability equivalence, warship, carrying spares, optimization

Minzhi RUAN, Hua LI, Junlong WANG. Mission-oriented dynamic optimization model of carrying spares for warship redundant system[J]. Journal of Systems Engineering and Electronics, 2018, 29(3): 539-548.

Figures/Tables 14

Fig 1

Fig 2

Table 1

Fig 3

Table 2

Table 3

Table 4

Fig 4

Fig 5

Fig 6

Fig 7

Fig 8

Fig 9

Table 5

References 29

1	SHERBROOKE C C. Optimal inventory modeling of systems: multi-echelon techniques. Boston: Artech House, 2004.
2	III A F L, CIARALLO F W. Reliability and operations: keys to lumpy aircraft spare parts demands. Journal of Air Transport Management, 2016, 50, 30- 40. doi: 10.1016/j.jairtraman.2015.09.004
3	RUAN M Z, WANG R, KONG Q F. Mission-oriented configuration model of aircraft carrying spares project and dynamic optimization policy. Transactions of Nanjing University of Aeronautics and Astronautics, 2016, 33 (5): 626- 632.
4	RUAN M Z, LI H, FU J. System optimization-oriented spare parts dynamic configuration model for multi-echelon multiindenture system. Journal of Systems Engineering and Electronics, 2017, 28 (5): 923- 933. doi: 10.21629/JSEE.2017.05.10
5	XIE J, LIU X D, DUAN B, et al. Research on the reserve model of two level recoverable spare parts under multiindenture. System Engineering-Theory & Practice, 2015, 35 (3): 811- 816.
6	GU J, ZHANG G, LI K. Efficient aircraft spare parts inventory management under demand uncertainty. Journal of Air Transport Management, 2015, 42, 101- 109. doi: 10.1016/j.jairtraman.2014.09.006
7	PATRIARCA R, COSTANTINO F, GRAVIO G D. Inventory model for a multi-echelon system with unidirectional lateral transshipment. Expert Systems with Applications, 2016, 65, 372- 382. doi: 10.1016/j.eswa.2016.09.001
8	RUAN M Z, LUO Y, LI H. Configuration model of partial repairable spares under batch ordering policy based on inventory state. Chinese Journal of Aeronautics, 2014, 27 (3): 558- 567. doi: 10.1016/j.cja.2014.04.021
9	HANBALI A A, HEIJDEN M V D. Interval availability analysis of a two-echelon multi-item system. European Journal of Operational Research, 2013, 228 (3): 494- 503.
10	COSTANTINO F, GRAVIO G D, TRONCI M. Multi-echelon, multi-indenture spare parts inventory control subject to system availability and budget constraints. Reliability Engineering and System Safety, 2013, 119, 95- 101. doi: 10.1016/j.ress.2013.05.006
11	ELISA A, MATTHIEU H. On two-echelon inventory systems with Poisson demand and lost sales. European Journal of Operational Research, 2014, 235 (1): 334- 338.
12	WANG S, LI Q M, PENG Y W. Dynamic management model of two-echelon maintenance and supply system for spare parts with cannibalization. Acta Aeronautica et Astronautica Sinica, 2013, 34 (6): 1326- 1335.
13	ZHANG T, GUO B, WU X Y, et al. Spare availability model for k-out-of-N system with different k in different phases. Acta Armanentarii, 2006, 27 (3): 485- 488.
14	XUE T, FENG Y X, QIN Q, et al. Optimization of repairable spare parts for K/N cold-standby redundant system considering scraps. Journal of South China University of Technology (Natural Science Edition), 2014, 42 (1): 41- 45.
15	HU T, YU J. Reliability optimization model of n/K(G) phased mission systems. Systems Engineering and Electronics, 2012, 34 (1): 217- 220.
16	WU X Y, WU X Y. Extended object-oriented Petri net model for mission reliability simulation of repairable PMS with common cause failures. Reliability Engineering and System Safety, 2015, 136, 109- 119. doi: 10.1016/j.ress.2014.11.012
17	LU L, YANG J P. Initial spare allocation method for k/N(G) structure system. Acta Aeronautica et Astronautica Sinica, 2014, 35 (3): 773- 778.
18	RUAN M Z, LI Q M, ZHANG G Y, et al. Optimization method of carrying spare parts support project for warship equipment under multi-constraints. Acta Armanentarii, 2013, 34 (9): 1144- 1149.
19	WEI S H, CHEN Y Q, JIN J S. Warship spare parts allotment optimization method under space and cost constraints. Systems Engineering and Electronics, 2013, 35 (12): 2540- 2544.
20	WANG N C, KANG R. An optimization model for inventory spares under multi-constraints and its decomposition algorithm. Acta Armanentarii, 2009, 30 (2): 247- 251.
21	BASTEN R J I, HOUTUM G J V. System-oriented inventory models for spare parts. Surveys in Operations Research and Management Science, 2014, 19 (1): 34- 55. doi: 10.1016/j.sorms.2014.05.002
22	ARTS J. A multi-item approach to repairable stocking and expediting in a fluctuating demand environment. European Journal of Operational Research, 2016, 256 (1): 102- 115.
23	SLEPTCHENKO A, HEIJDEN M V D. Joint optimization of redundancy level and spare part inventories. Reliability Engineering and System Safety, 2016, 153, 64- 74. doi: 10.1016/j.ress.2016.04.006
24	RUAN M Z, LIU R Y. Configuration and optimization model for multi-indenture spares with lateral transshipments under stochastic demand. System Engineering -Theory & Practice, 2016, 36 (10): 2689- 2698.
25	SRIVASTAV A, AGRAWAL S. Multi-objective optimization of hybrid backorder inventory model. Expert Systems with Applications, 2016, 51, 76- 84. doi: 10.1016/j.eswa.2015.12.032
26	BHUNIA A K, SHAIKH A A. An application of PSO in a two-warehouse inventory model for deteriorating item under permissible delay in payment with different inventory policies. Applied Mathematics and Computation, 2015, 256, 831- 850. doi: 10.1016/j.amc.2014.12.137
27	HORENBEEK A V, SCARF P A, CAVALCANTE C A V, et al. The effect of maintenance weight on spare parts inventory for a fleet of assets. IEEE Trans. on Reliability, 2013, 62(3): 596-607.
28	GHADDAR B, SAKR N, ASIEDU Y, et al. Spare parts stocking analysis using genetic programming. European Journal of Operational Research, 2016, 252 (1): 136- 144.
29	NOWICKIABA D R. Improving the computational efficiency of metric-based spares algorithms. European Journal of Operational Research, 2012, 219 (2): 324- 334.

Equipment	Spare	Structure	Spares	Initial parameter of reliability-maintainability-supportability (RMS)
Equipment	Spare	code	type	MTBF/h	$MRT_{0}$/h	$MRT_{i}$/h	$NR_{0}$	$NR_{i}$	$M$/kg	$V$/m$^{3}$	$C$/10$^{4}$
	Power amplifier	1	LRU	1 200	32	16	0.35	0.75	39	0.9	7.2
	Voltage regulator	1.1	SRU	1 300	12	8	0.25	0.75	3	0.5	2.6
Alert-searching	Drive set	1.2	SRU	1 700	12	8	0.3	0.6	7	0.4	3.4
radar	Signal unit	2	LRU	1 350	16	12	0.3	0.65	12	0.3	8.1
	Sampling plate	2.1	SRU	1 500	12	6	0.3	0.7	4	0.15	3.5
	Output drive plate	2.2	SRU	1 600	8	6	0.35	0.65	7	0.15	4.6
	Fire control unit	3	LRU	1000	36	32	0.35	0.75	18	0.4	5.7
	Interface convertor	3.1	SRU	1350	12	12	0.25	0.75	2	0.22	1.2
Air defense	I/O panel	3.2	SRU	1 500	12	12	0.4	0.6	0.5	0.22	3.1
weapon system	Launch & control	4	LRU	1 000	12	12	0.25	0.7	12	0.5	8.4
	CPU module	4.1	SRU	1 200	12	8	0.3	0.7	0.5	0.35	4.3
	Timing panel	4.2	SRU	1 250	12	8	0.35	0.65	3	0.01	3.2
	Antenna array	5	LRU	875	42	40	0.25	0.75	24	2.2	7.8
	Interface card	5.1	SRU	1 000	12	6	0.35	0.65	1	0.15	4.6
Electronic warfare	Microwave module	5.2	SRU	1 300	24	18	0.3	0.3	3	0.1	2.6
defense system	Display controller	6	LRU	1 250	16	16	0.3	0.8	9	0.5	7.5
	Power module	6.1	SRU	1 400	8	6	0.35	0.5	2	0.15	3.4
	Display panel	6.2	SRU	1 780	12	12	0.3	0.7	1	0.05	2.2

Mission phase	Equipment	Start time	Duration	Install quantity	Working quan-tity minimum
	Alert-searching radar	0	8	3	2
Shipping	Air weapon system	—	—	2	—
	Electronic warfare	—	—	2	—
	Alert-searching radar	8	4	3	3
Air defense anti-missile	Air weapon system	9	3	2	2
	Electronic warfare	—	—	4	—
	Alert-searching radar	12	5	3	2
Electronic warfare defense	Air weapon system	—	—	2	—
	Electronic warfare	14	4	4	3
	Alert-searching radar	18	12	3	1
Returning	Air weapon system	—	—	2	—
	Electronic warfare	18	8	4	1

Project	Result analysis	$\rho_{s}$	Cost/million Yuan	Weight/kg	Volume/m$^{3}$
Cost	Value	0.956 3	2.625	559.5	30.18
Cost	Satisfied?	Y	—	N	N
Weight	Value	0.950 3	3.192	526.5	28
Weight	Satisfied?	Y	—	Y	N
Volume	Value	0.966 2	3.009	639.5	26.04
Volume	Satisfied?	Y	—	N	Y
Total	Value	0.950 8	2.71	514	25.1
scale	Satisfied?	Y	—	Y	Y

Spare part	Cost	Weight	Volume	Total scale
Power amplifier	4	4	7	4
Voltage regulator	4	4	1	4
Drive sets	0	1	0	1
Signal unit	3	4	4	4
Sampling plate	1	4	4	2
Output drive plate	0	0	1	0
Fire control unit	3	2	3	3
Interface convertor	1	4	0	1
I/O panel	0	4	0	1
Launch & control	3	3	3	3
CPU module	1	5	1	1
Timing panel	1	4	4	1
Antenna array	7	4	4	4
Interface card	1	5	4	4
Microwave module	4	5	4	4
Display controller	7	7	7	7
Power module	1	4	4	4
Display panel	1	4	4	4

Spares project	Modelresult	VMETRICresult	Simulationresult	Error/%
Cost project	0.956 3	0.965 4	0.982 6	2.8
Weight project	0.950 3	0.959 9	0.974 5	2.5
Volume project	0.966 2	0.980 1	0.988 9	2.3
Total scale project	0.950 8	0.960 3	0.976 3	2.7

Mission-oriented dynamic optimization model of carrying spares for warship redundant system

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 14

References 29

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Jun CHEN, Xudong GAO, Jia RONG, Xiaoguang GAO. A situation awareness assessment method based on fuzzy cognitive maps [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1108-1122.
[2]	Bing WANG, Pengfei ZHANG, Yufeng HE, Xiaozhi WANG, Xianxia ZHANG. Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1143-1150.
[3]	Honghong ZHANG, Xusheng GAN, Shuangfeng LI, Zhiyuan CHEN. UAV safe route planning based on PSO-BAS algorithm [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1151-1160.
[4]	Weining MA, Fei ZHAO, Xin LI, Qiwei HU, Bingcong SHANG. Joint optimization of inspection-based and age-based preventive maintenance and spare ordering policies for single-unit systems [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1268-1280.
[5]	Jianwei SUN, Chao WANG, Qingzhan SHI, Wenbo REN, Zekun YAO, Naichang YUAN. Intelligent optimization methods of phase-modulation waveform [J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 916-923.
[6]	Fuyunxiang YANG, Leping YANG, Yanwei ZHU, Xin ZENG. A DNN based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft [J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 438-446.
[7]	Zheng WANG, Zhiyuan HU, Xuanfang YANG. Multi-agent and ant colony optimization for ship integrated power system network reconfiguration [J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 489-496.
[8]	Jingfeng LI, Yunxiang CHEN, Zhongyi CAI, Zezhou WANG. A dynamic condition-based maintenance optimization model for mission-oriented system based on inverse Gaussian degradation process [J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 474-488.
[9]	Zihang DING, Junwei XIE, Zhengjie LI. Adaptive transmit beamspace optimization design based on RD-log-FDA radar [J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 91-96.
[10]	Tianwei WU, Siguang AN, Jianqiang HAN, Nanying SHENTU. An ε -domination based two-archive 2 algorithm for many-objective optimization [J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 156-169.
[11]	Wanping SONG, Zengqiang CHEN, Mingwei SUN, Qinglin SUN. Reinforcement learning based parameter optimization of active disturbance rejection control for autonomous underwater vehicle [J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 170-179.
[12]	Jiaxin HU, Leping YANG, Huan HUANG, Yanwei ZHU. Optimal reconfiguration of constellation using adaptive innovation driven multiobjective evolutionary algorithm [J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1527-1538.
[13]	Zining WANG, Min LIN, Xiaogang TANG, Kefeng GUO, Shuo HUANG, Ming CHENG. Multi-objective robust secure beamforming for cognitive satellite and UAV networks [J]. Journal of Systems Engineering and Electronics, 2021, 32(4): 789-798.
[14]	Yanan DU, Hongyuan GAO, Menghan CHEN. Direction of arrival estimation method based on quantum electromagnetic field optimization in the impulse noise [J]. Journal of Systems Engineering and Electronics, 2021, 32(3): 527-537.
[15]	Ruiyang LI, Ming HE, Hongyue HE, Zhixue WANG, Cheng YANG. A branch and price algorithm for the robust WSOS scheduling problem [J]. Journal of Systems Engineering and Electronics, 2021, 32(3): 658-667.