This chapter introduces the simulation of queueing systems and related stochastic models, providing both theoretical foundations and practical methods for analyzing complex systems where analytical solutions are often unavailable. Key applications include classical models such as the repairman problem, Markovian queues (M/M/1, M/M/c, M/G/ \(\infty \) ), GI/G/1 and GI/G/c systems, as well as telecommunication networks and random access protocols like ALOHA. The chapter covers the modeling and simulation of arrival processes—focusing on the Poisson process (homogeneous, non-homogeneous, and on general state spaces)—and renewal processes as input models for queueing systems. The principles of discrete-event simulation are explained and illustrated with examples, highlighting event-driven methods and their efficiency in simulating the evolution of systems such as queues and the repairman problem. Emphasis is placed on the study of system characteristics under steady-state conditions, including average queue length, waiting time, system utilization, and other key performance measures. Both stable and unstable systems are discussed, with practical guidance on output analysis, confidence intervals, and error assessment. The concept of asymptotic variance is introduced as the natural analogue to the variance estimated from independent replications when analyzing processes with continuous sample paths. By the end of the chapter, readers will be equipped to simulate, analyze, and interpret results for a broad class of queueing and related models, gaining insight into the interplay between theoretical models and empirical, simulation-based solutions.