Network flow Optimization through Monte Carlo Simulation

We encounter network flows in day to day life. They are the backbone of logistics, city planning, processes etc. In order to study these networks, domain specific connectivity graphs along with their historical observations are used. Traditionally, birth & death process, Little's law, Burke's theorem, etc. have been applied to analyze various network flow scenarios. In this paper, we approach similar problems using Monte Carlo simulations, Markov Chain and Queuing Theory, which provide an edge over traditional methods in case of high dimensionality of multiple nodes. The methodologies described in paper can be applied to various situations of business formulations: Load/traffic balancing, Queue reduction, Network Anomaly detection, etc. This paper provides an effective tool for designing, diagnosing, monitoring & predictions of process of networks. Networks flow are the backbone of logistics, city planning, processes, etc.