TY - JOUR

T1 - A novel approach to predict network reliability for multistate networks by a deep neural network

AU - Huang, Cheng Hao

AU - Huang, Ding Hsiang

AU - Lin, Yi Kuei

N1 - Publisher Copyright:
© 2021 International Chinese Association of Quantitative Management.

PY - 2021/10/12

Y1 - 2021/10/12

N2 - Real-world systems, such as manufacturing or computer systems, can be modeled as multistate network (MSN) consisting of arcs with stochastic capacity. Network reliability for an MSN is described as the probability that the system can meet the demand. The network reliability for demand level d can be computed in terms of the minimal path (calledd-MP). However, efficiently calculating network reliability is challenging in large-scale networks. Deep learning approaches are rapidly advancing several areas of technology, with significant applications in image recognition, parameter adjustment, and autonomous driving. Hence, in this study, we adopt a deep neural network (DNN) model to predict network reliability for a given demand level. To train the DNN model, network information is first used as input data. Then, a DNN model is constructed, including the determination of related functions. Furthermore, Bayesian optimization (BO) is applied to determine related hyperparameters. A practical implementation using a bridge network demonstrates the feasibility of the DNN model. Finally, experiments involving two networks with more nodes and arcs indicate the computational efficiency of combining deep learning methods and the existing d-MP algorithm.

AB - Real-world systems, such as manufacturing or computer systems, can be modeled as multistate network (MSN) consisting of arcs with stochastic capacity. Network reliability for an MSN is described as the probability that the system can meet the demand. The network reliability for demand level d can be computed in terms of the minimal path (calledd-MP). However, efficiently calculating network reliability is challenging in large-scale networks. Deep learning approaches are rapidly advancing several areas of technology, with significant applications in image recognition, parameter adjustment, and autonomous driving. Hence, in this study, we adopt a deep neural network (DNN) model to predict network reliability for a given demand level. To train the DNN model, network information is first used as input data. Then, a DNN model is constructed, including the determination of related functions. Furthermore, Bayesian optimization (BO) is applied to determine related hyperparameters. A practical implementation using a bridge network demonstrates the feasibility of the DNN model. Finally, experiments involving two networks with more nodes and arcs indicate the computational efficiency of combining deep learning methods and the existing d-MP algorithm.

KW - bayesian optimization (BO)

KW - deep neural network (DNN)

KW - multistate network (MSN)

KW - network reliability

KW - Prediction

UR - http://www.scopus.com/inward/record.url?scp=85116870199&partnerID=8YFLogxK

U2 - 10.1080/16843703.2021.1992072

DO - 10.1080/16843703.2021.1992072

M3 - Article

AN - SCOPUS:85116870199

SN - 1684-3703

JO - Quality Technology and Quantitative Management

JF - Quality Technology and Quantitative Management

ER -