Artificial neural network simulation for markovian queuing models

Abstract

Successful ranking of a website by Google or electric charging of vehicle, congestion is pervasive in all domains. This implies that the presence of queues everywhere or in various places simultaneously. Under this environment, a good understanding of the relationship between queueing and delay is essential in the design of mathematical queuing models. However, uncertainty is an unavoidable phenomenon in any decision-making process. Good number of mathematical approaches has been presented in the literature to the analysis of queuing. Uncertainty is usually considered as unidimensional in nature that can be handled with probability theory. The objective of queuing analysis is to offer a reasonably satisfactory service to waiting customers. Queuing theory is not an optimization technique. Rather, it determines the measure of performance of waiting lines, such as the average waiting time in the queue and the productivity of the service facility, which can then be used to design the service installation. Assumed systems and systems that are too complicated to be disturbed are often difficult to study by analytical techniques. Simulation is one technique that can be seen successfully utilized for analyzing such systems. Artificial neural networks (ANN) form a branch of artificial intelligence. Neural networks represent a connection of simple processing elements capable of processing information in response to external inputs. In this work, such a Markovian queue is simulated using ANN and presented the result. The result shows that the ANN is capable of solving complex queuing problems.