RESEARCH PAPER
Defensive strategy optimization of consecutive-k-out-of-n systems under deterministic external risks
| 1 | Xi’an University of Science and Technology, Department of Intelligence Manufacturing, School of Mechanical Engineering, No. 58 Yanta Road, Beilin District, Xi’an Shaanxi, 710054, China |
| 2 | Northwestern Polytechnical University, Department of Industrial Engineering, School of Mechanical Engineering, No. 127 West Youyi Road, Beilin District, Xi’an Shaanxi, 710072, P.R.China |
Publication date: 2022-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(2):306–316
HIGHLIGHTS
- A defensive capability based on real-time reliability is developed for Con/k/n systems.
- Defensive importance measure is constructed to optimize component redundancy locally.
- The effectiveness of DIMGA is verified by comparing it with CGA under 36 scenarios.
- Con/k/5 systems’ redundancy distribution rule under spacing/continuous risk is analyzed.
KEYWORDS
consecutive-k-out-of-n systemexternal risksdefensive capabilitystrategy optimizationredundancy distribution
ABSTRACT
Consecutive-k-out-of-n (Con/k/n) system, a reconfigurable system, can improve the system performance by adjusting the redundancy and assignment of components. This paper aims to determine the optimal defensive strategy of Con/k/n systems under external risks. The defensive capability of Con/k/n systems is evaluated based on real-time system reliability, and a defensive importance measure (DIM) is constructed to optimize components’ redundancy locally. To solve the proposed optimization model effectively, a DIM-based genetic algorithm (DIGA) is developed by integrating the advantages of DIM’s local search with the global search ability of the classical genetic algorithm (CGA). The numerical experiment under 36 scenarios illustrates that DIGA is more effective than CGA verified by average defensive capability, robustness, and convergence generations. Moreover, the redundancy distribution analysis of Con/k/5 systems in the optimal defensive strategy shows that the redundancy of F(G) systems is in a spaced (continuous) way under spacing k-1 risk or continuous k risk.
REFERENCES (43)
1.
Ahmadian N, Lim G J, Cho J, Bora S. A quantitative approach for assessment and improvement of network resilience. Reliability Engineering & System Safety 2020; 200: 106977, https://doi.org/10.1016/j.ress....
2.
Almoghathawi Y, Barker K. Component importance measures for interdependent infrastructure network resilience. Computers and Industrial Engineering 2019; 133: 153-164, https://doi.org/10.1016/j.cie.....
3.
Anwar G A, Dong Y, Zhai C. Performance-based probabilistic framework for seismic risk, resilience, and sustainability assessment of reinforced concrete structures. Advances in Structural Engineering 2020; 23(7): 1454-1472, https://doi.org/10.1177/136943....
4.
Aziz T, Lin Z, Waseem M, Liu S. Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience. International Transactions on Electrical Energy Systems 2021; 31(3): e12704, https://doi.org/10.1002/2050-7....
5.
Bai G, Wang H, Zheng X et al. Improved resilience measure for component recovery priority in power grids. Frontiers of Engineering Management 2021; 8(4): 545-556, https://doi.org/10.1007/s42524....
6.
Balakrishnan S, Zhang Z. Criticality and Susceptibility Indexes for Resilience-Based Ranking and Prioritization of Components in Interdependent Infrastructure Networks. Journal of Management in Engineering 2020; 36(4): 04020022, https://doi.org/10.1061/(ASCE) ME.1943-5479.0000769.
7.
Bukowski L, Werbińska-Wojciechowska S. Using fuzzy logic to support maintenance decisions according to resilience-based maintenance concept. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23 (2): 294-307, https://doi.org/10.17531/ein.2....
8.
Che H, Zeng S, Guo J. A reliability model for load-sharing k -out-of- n systems subject to soft and hard failures with dependent workload and shock effects. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(2): 253-264, https://doi.org/10.17531/ein.2....
9.
Che H, Zeng S, You Q et al. A fault tree-based approach for aviation risk analysis considering mental workload overload. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(4): 646-658, https://doi.org/10.17531/ein.2....
10.
Dui H, Zheng X, Wu S. Resilience analysis of maritime transportation systems based on importance measures. Reliability Engineering and System Safety 2021; 209: 107461, https://doi.org/10.1016/j.ress....
11.
Fang Y P, Zio E. An adaptive robust framework for the optimization of the resilience of interdependent infrastructures under natural hazards. European Journal of Operational Research 2019; 276(3): 1119-1136, https://doi.org/10.1016/j.ejor....
12.
Ghorbani-Renani N, González A D, Barker K, Morshedlou N. Protection-interdiction-restoration: Tri-level optimization for enhancing interdependent network resilience. Reliability Engineering and System Safety 2020; 199: 106907, https://doi.org/10.1016/j. ress.2020.106907.
13.
Jaiswal D P, Anand H, Srinivasan S M, Darayi M. A Data-Driven Model to Generate Disruptive Scenarios for Infrastructure Resilience Studies. Procedia Computer Science 2021; 185: 248-255, https://doi.org/10.1016/j.proc....
14.
Kakadia D, Ramirez-Marquez J E. Quantitative approaches for optimization of user experience based on network resilience for wireless service provider networks. Reliability Engineering & System Safety 2020; 193: 106606, https://doi.org/10.1016/j.ress....
15.
Kuo W, Zhang W, Zuo M. Consecutive-k-out-of-n:G system: The mirror image of a consecutive-k-out-of-n:F system. IEEE Transactions on Reliability 1990; 39(2): 244-253, https://doi.org/10.1109/24.558....
16.
Levitin G, Xing L, Dai Y. Linear multistate consecutively-connected systems subject to a constrained number of gaps. Reliability Engineering & System Safety 2015; 133: 246-252, https://doi.org/10.1016/j.ress....
17.
Li M, Hu L, Peng R, Bai Z. Reliability modeling for repairable circular consecutive-k-out-of-n: F systems with retrial feature. Reliability Engineering & System Safety 2021; 216: 107957, https://doi.org/10.1016/j.ress....
18.
Li R, Dong Q, Jin C, Kang R. A new resilience measure for supply chain networks. Sustainability 2017; 9(1): 144, https://doi.org/10.3390/su9010....
19.
Li R, Gao Y. On the component resilience importance measures for infrastructure systems. International Journal of Critical Infrastructure Protection 2021: 100481, https://doi.org/10.1016/j.ijci....
20.
Liao T Y, Hu T Y, Ko Y N. A resilience optimization model for transportation networks under disasters. Natural Hazards 2018; 93(1): 469-489, https://doi.org/10.1007/s11069....
21.
Lin Y-K, Huang C-F. Assessment of spare reliability for multi-state computer networks within tolerable packet unreliability. International Journal of Systems Science 2015; 46(6): 1020-1035, https://doi.org/10.1080/002077....
22.
Liu R-P, Lei S, Peng C et al. Data-based resilience enhancement strategies for electric-gas systems against sequential extreme weather events. IEEE Transactions on Smart Grid 2020; 11(6): 5383-5395, https://doi.org/10.1109/TSG.20....
23.
Liu X, Fang Y-P, Zio E. A hierarchical resilience enhancement framework for interdependent critical infrastructures. Reliability Engineering & System Safety 2021; 215: 107868, https://doi.org/10.1016/j.ress....
24.
Lu Q-C. Modeling network resilience of rail transit under operational incidents. Transportation Research Part A: Policy and Practice 2018; 117: 227-237, https://doi.org/10.1016/j.tra.....
25.
Ma C, Wang Q, Cai Z et al. Component reassignment for reliability optimization of reconfigurable systems considering component degradation. Reliability Engineering and System Safety 2021; 215: 107867, https://doi.org/10.1016/j.ress....
26.
Moslehi S, Reddy T A. Sustainability of integrated energy systems: A performance-based resilience assessment methodology. Applied Energy 2018; 228: 487-498, https://doi.org/10.1016/j.apen....
27.
Najarian M, Lim G J. Optimizing infrastructure resilience under budgetary constraint. Reliability Engineering and System Safety 2020; 198: 106801, https://doi.org/10.1016/j.ress....
28.
Ozkan A, Kesik T, Yilmaz A Z, O'Brien W. Development and visualization of time-based building energy performance metrics. Building Research & Information 2019; 47(5): 493-517, https://doi.org/10.1080/096132....
29.
Qu L, Han C, Li Y et al. Recent Advances in the Reliability Evaluation and Optimization of Linear Multistate Consecutively-connected Systems. Recent Patents on Engineering 2021; 15(3): 314-325, https://doi.org/10.2174/187221....
30.
Ramezankhani M J, Torabi S A, Vahidi F. Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach. Computers and Industrial Engineering 2018; 126: 531-548, https://doi.org/10.1016/j.cie.....
31.
Rosato V, Di Pietro A, Kotzanikolaou P et al. Integrating resilience in time-based dependency analysis: a large-scale case study for urban critical infrastructures. Issues on Risk Analysis for Critical Infrastructure Protection, IntechOpen: 2021, 91-110, https://doi.org/10.5772/intech....
32.
Si S, Zhao J, Cai Z, Dui H. Recent advances in system reliability optimization driven by importance measures. Frontiers of Engineering Management 2020; 7(3): 335-358, https://doi.org/10.1007/s42524....
33.
Szaciłło L, Jacyna M, Szczepański E, Izdebski M. Risk assessment for rail freight transport operations. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(3): 476-488, https://doi.org/10.17531/ein.2....
34.
Tran H T, Balchanos M, Domerçant J C, Mavris D N. A framework for the quantitative assessment of performance-based system resilience. Reliability Engineering and System Safety 2017; 158(February 2016): 73-84, https://doi.org/10.1016/j.ress....
35.
Vintr Z, Malach J. Selected aspects of physical structures vulnerability-state-of-the-art. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2012; 14 (3): 189-194.
36.
Wang D, Si S, Cai Z, Zhao J. Reliability optimization of linear consecutive-k-out-of-n: F systems driven by reconfigurable importance. Reliability Engineering & System Safety 2021; 216: 107994, https://doi.org/10.1016/j.ress....
37.
Wang Y, Guo L, Wen M, Yang Y. Availability analysis for a multi-component system with different k-out-of-n: G warm standby subsystems subject to suspended animation. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(2): 289-300, https://doi.org/10.17531/ein.2....
38.
Wu C, Pan R, Zhao X, Cao S. Reliability evaluation of consecutive-k-out-of-n: F systems with two performance sharing groups. Computers and Industrial Engineering 2021; 153: 107092, https://doi.org/10.1016/j.cie.....
39.
Wu G, Li Z S. Cyber Physical Power System (CPPS): A review on measures and optimization methods of system resilience. Frontiers of Engineering Management 2021; 8: 503-518, https://doi.org/10.1007/s42524....
40.
Xiang Y, Levitin G, Dai Y. Linear multistate consecutively-connected systems with gap constraints. IEEE Transactions on Reliability 2012; 61(1): 208-214, https://doi.org/10.1109/TR.201....
41.
Xu Z, Ramirez-Marquez J E, Liu Y, Xiahou T. A new resilience-based component importance measure for multi-state networks. Reliability Engineering and System Safety 2020; 193: 106591, https://doi.org/10.1016/j.ress....
42.
Zhang C, Zhang Y, Dui H et al. Importance measure-based maintenance strategy considering maintenance costs. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2022; 24 (1): 15-24, https://doi.org/10.17531/ein.2....
43.
Zhang Q, Wang Z, Ma S, Arif A. Stochastic pre-event preparation for enhancing resilience of distribution systems. Renewable and Sustainable Energy Reviews 2021; 152: 111636, https://doi.org/10.1016/j.rser....
CITATIONS (1):
1.
Start-Up Strategy-Based Resilience Optimization of Onsite Monitoring Systems Containing Multifunctional Sensors
Jiangbin Zhao, Zaoyan Zhang, Mengtao Liang, Xiangang Cao, Zhiqiang Cai
Mathematics
Jiangbin Zhao, Zaoyan Zhang, Mengtao Liang, Xiangang Cao, Zhiqiang Cai
Mathematics
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.