#4888. A novel approach to predict network reliability for multistate networks by a deep neural network

August 2026publication date
Proposal available till 02-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Industrial Relations;
Business and International Management;
Management of Technology and Innovation;
Management Science and Operations Research;
Information Systems and Management;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №4888.1 Contract №4888.2 Contract №4888.3 Contract №4888.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

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). 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. Then, a DNN model is constructed, including the determination of related functions. 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:
bayesian optimization (BO); deep neural network (DNN); multistate network (MSN); network reliability; Prediction

Contacts :
0