摘要
In the traditional multi-state flow networks (MFNs), it is assumed that the flow is fixed in each arc. However, the flow may experience gain after transmission via arcs in many real-life applications; e.g., the infected population size is increased from time to time for a certain period during outbreaks of disease, the number of bit errors is amplified in digital transmission, etc. Hence, a novel network model called the learning-effect MFN (MFN le ) is proposed to meet real-world problems. A straightforward and simple algorithm based on minimal path (MP) set is presented here to evaluate MFN le reliability, which is defined as the probability that at least d units of data can be sent from the source node and d out (≥d) units of data exists from the sink node through a single MP in the MFN le . The computational complexity of the proposed algorithm is also analyzed. Finally, an example is given to illustrate how the MFN le reliability is calculated using the proposed algorithm.