Topological optimization of reliable networks under dependent failures

Barrera, Javiera; Cancela, Héctor; Moreno, Eduardo

doi:10.1016/j.orl.2014.12.014

Cited by 35 publications

(17 citation statements)

References 24 publications

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“…Also, another interesting venue of work is to tackle models with dependency among the links' states (which, as works such as [23] explain, can have an important impact in reliability values). In particular, it seems possible to apply the proposed method to cases where the dependence can be represented by a set of exponential random variables, like the case of the Marshall-Olkin copula distributions (arising from shock -and anti-shock-models with exponential time arrivals), which have been studied in the case of traditional network reliability in [24], [25] and [26], and can be extended to multi-level link capacity distributions. , 2009), and co-authored several of its chapters.…”

Section: Discussionmentioning

confidence: 99%

Efficient Estimation of Stochastic Flow Network Reliability

2019

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The Creation Process is an algorithm that transforms a static network model into a dynamic one. It is the basis of different variance reduction methods designed to make efficient reliability estimations on highly reliable networks in which links can only assume two possible values, operational or failed. In this article the Creation Process is extended to let it operate on network models in which links can assume more than two values. The proposed algorithm, called here Multi-Level Creation Process, is the basis of a method, also introduced here, to make efficient reliability estimations of highly reliable stochastic flow networks. The method proposed, which consists in an application of Splitting over the Multi-Level Creation Process, is empirically shown to be accurate, efficient, and robust.

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Section: Discussionmentioning

confidence: 99%

Efficient Estimation of Stochastic Flow Network Reliability

2019

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“…Research that examines the optimisation of a network’s reliability is concerned with maximising the probability that a network will remain connected after disruptions (Colbourn 1987 , Shier 1991 , Shooman 2002 , Barrera et a 2015 ). However creation of network vulnerability (or conversely, reliability) measures for supply networks is not straightforward because of key differences between these models and the supply network application domain.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimisation of reliable product-plant network configuration

Brintrup

Puchkova

2018

Appl Netw Sci

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Ensuring manufacturing reliability is key to satisfying product orders when production plants are subject to disruptions. Reliability of a supply network is closely related to the redundancy of products as production in disrupted plants can be replaced by alternative plants. However the benefits of incorporating redundancy must be balanced against the costs of doing so. Models in literature are highly case specific and do not consider complex network structures and redundant distributions of products over suppliers, that are evident in empirical literature. In this paper we first develop a simple generic measure for evaluating the reliability of a network of plants in a given product-plant configuration. Second, we frame the problem as a multi-objective evolutionary optimisation model to show that such a measure can be used to optimise the cost-reliability trade off. The model has been applied to a producer’s automotive light and lamp production network using three popular genetic algorithms designed for multi-objective problems, namely, NSGA2, SPEA2 and PAES. Using the model in conjunction with genetic algorithms we were able to find trade off solutions successfully. NSGA2 has achieved the best results in terms of Pareto front spread. Algorithms differed considerably in their performance, meaning that the choice of algorithm has significant impact in the resulting search space exploration.

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“…A different type of simplification is applied in [15], where the authors study the problem of maximizing the reliability of a network under budget constraints and propose an integer formulation using sample average approximation.…”

Section: Introductionmentioning

confidence: 99%

Optimization algorithms for resilient path selection in networks

Casazza

Ceselli

2021

Computers & Operations Research

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We study a Resilient Path Selection Problem (RPSP) arising in the design of communication networks with reliability guarantees. A graph is given, in which every arc has a cost and a probability of failure, and in which two nodes are marked as source and destination. The aim of our RPSP is to find a subgraph of minimum cost, containing a set of paths from the source to the destination nodes, such that the probability that all paths fail simultaneously is lower than a given threshold. We explore its theoretical properties and show that, despite a few interesting special cases can be solved in polynomial time, it is in general NP-hard. In fact, we prove that even deciding if a given subgraph has a probability of failure not exceeding a given threshold is already NP-Complete. We therefore introduce an integer relaxation that simplifies the computation of such probability, and we design an exact algorithm for the full RPSP exploiting this relaxation and other ad-hoc procedures. We present computational results, highlighting that our exact algorithms can handle graphs with up to 30 nodes within minutes of computing time, consistently producing proven optimal solutions. Moreover, we show that our algorithms can be used also as heuristics, outperforming path protection schemes from the literature also on much larger networks.

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Topological optimization of reliable networks under dependent failures

Cited by 35 publications

References 24 publications

Efficient Estimation of Stochastic Flow Network Reliability

Efficient Estimation of Stochastic Flow Network Reliability

Multi-objective optimisation of reliable product-plant network configuration

Optimization algorithms for resilient path selection in networks

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