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

ABSTRACT

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.

KEYWORDS:

• Prediction
• multistate network (MSN)
• network reliability
• deep neural network (DNN)
• bayesian optimization (BO)

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan [MOST 109-2221-E-009-067-MY3].

Notes on contributors

Cheng-Hao Huang

Cheng-Hao Huang is currently a Ph.D. student of Industrial Engineering and Management Department, National Yang-Ming Chiao Tung University, Hsinchu, Taiwan. He received the B.S. degree from National Chiao Tung University, in 2019. His research interests include network reliability, performance evaluation, and production management.

Ding-Hsiang Huang

Ding-Hsiang Huang received the Ph.D. degree in Industrial Engineering and Management Department, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, in 2021, the M.S. degree from National Taiwan University of Science and Technology, Taipei, Taiwan, in 2015, and His research interests include network reliability, performance evaluation, production management, machine learning, and soft computing.

Yi-Kuei Lin

Yi-Kuei Lin received the Bachelor’s degree in the Department of Applied Mathematics from National Chiao Tung University, Hsinchu, Taiwan, in 1993; and the Master’s and Ph.D. degrees in the Department of Industrial Engineering and Management from National Tsing Hua University, Hsinchu, Taiwan, in 1995 and 1998, respectively.He is currently a Chair Professor in the Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. He has published more than 200 papers in refereed journals. His current research interests include performance evaluation, stochastic network reliability, operations research, and telecommunication management.