Adaptive network reliability analysis: Methodology and applications to power grid

Dehghani, Nariman L.; Zamanian, Soroush; Shafieezadeh, Abdollah

doi:10.1016/j.ress.2021.107973

Cited by 32 publications

(7 citation statements)

References 54 publications

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“…Reliability refers to the ability of a supply chain network to consistently deliver products, services, and information in a dependable and predictable manner, even in the face of disruptions, uncertainties, and challenges [3]. The importance of reliability in supply chain networks lies in its direct impact on overall performance, customer satisfaction, and profitability.…”

Section: Network Analysis Of Supply Chainmentioning

confidence: 99%

Risk Assessment of Supply Chain Network Through Subset Simulation-Based Reliability Analysis

Liang

2023

Digitalization and Management Innovation II

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In this paper, a subset simulation-based approach is proposed to tackle the network reliability analysis problem in supply chain risk assessment. The reliability of the supply chain is essential for seamless operations in modern businesses. Network reliability analysis evaluates the continuity and reliability of business operations in the event of failures occurring in nodes and paths within the supply chain system. Traditional methods for network reliability analysis often rely on computationally expensive Monte Carlo simulation due to the complexities and high-dimensional input space of supply chain systems. To overcome this challenge, a subset simulation-based approach is proposed in this paper. Subset simulation is an efficient method which employs importance sampling and Bayesian inference to accurately estimate the probability of extreme events. To implement the approach, the supply chain system is modeled as a network and sample uncertainties associated with input parameters. Subsequently, subset simulation techniques are utilized to generate a set of samples and evaluate the reliability of the supply chain system and assess potential risks. To validate the effectiveness of the proposed method, experiments are conducted on a real-world supply chain system. The results demonstrate that the approach achieves superior computational efficiency compared to traditional Monte Carlo simulation methods, while accurately assessing risks within the supply chain system.

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Section: Network Analysis Of Supply Chainmentioning

confidence: 99%

Risk Assessment of Supply Chain Network Through Subset Simulation-Based Reliability Analysis

Liang

2023

Digitalization and Management Innovation II

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“…For rare event estimation, crude MCS is impractical when the limit state function is expensive to compute, and hence, advanced variance reduction techniques or meta-models have been proposed. These techniques include the standard subset simulation (SuS) [7]- [9], various creation process methods [10]- [12], and actively trained meta-models [13], [14], while opportunities for new methods remain.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Monte Carlo methods for estimating rare events in power grids

Chan,

Paredes,

Papaioannou

et al. 2024

Preprint

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“…Infrastructures, such as power grids and transportation networks, are systems with complex network topology whose reliability assessment can be done by traditional methods, such as fault trees or reliability block diagrams, only if the size is relatively small or if the system can be decomposed in simple parts [1]. For realistically large networks instead, the reliability problem complexity increases exponentially with the network size [2].…”

Section: Introductionmentioning

confidence: 99%

Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures

Maio

Pettorossi

Zio

2023

Reliability Engineering & System Safety

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Adaptive network reliability analysis: Methodology and applications to power grid

Cited by 32 publications

References 54 publications

Risk Assessment of Supply Chain Network Through Subset Simulation-Based Reliability Analysis

Risk Assessment of Supply Chain Network Through Subset Simulation-Based Reliability Analysis

Adaptive Monte Carlo methods for estimating rare events in power grids

Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures

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