A Monte Carlo sampling plan for estimating network reliability
Operations Research
The Combinatorics of Network Reliability
The Combinatorics of Network Reliability
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A new Chance-Variance optimization criterion for portfolio selection in uncertain decision systems
Expert Systems with Applications: An International Journal
Economic-based resource allocation for reliable Grid-computing service based on Grid Bank
Future Generation Computer Systems
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Network reliability is very important for the decision support information. Monte Carlo Simulation (MCS) is one of the optimal algorithms to estimate the network reliability for different kinds of network configuration. The traditional reliability estimation requires the information of all Minimal Paths (MPs) or Minimal Cuts (MCs). However, finding all MPs/MCs is extremely computationally expensive. This paper has compared and analyzed three Monte Carlo Simulation (MCS) methods for estimating the two-terminal network reliability of a binary-state network: (1) MCS1 simulates the network reliability in terms of known MPs, (2) MCS2 estimates the network reliability in terms of known MCs; and (3) CAMCS (based on cellular automata, CA) estimates the network reliability directly without knowing any information of MPs or MCs. Our simulation results show that the direct estimation without knowing any information of MPs or MCs can speedup about 185 times when compared with other traditional approaches which require MPs or MCs information.