This document synthesizes key concepts, methodologies, and applications of Modelling and Simulation (M&S). M&S is a powerful discipline for analyzing real-world systems by creating representative models and operating them over time or space to study system performance and predict the effects of changes. The field has evolved significantly, from the Monte Carlo method developed in the 1940s to modern web-based simulations with advanced graphical interfaces.

The successful implementation of M&S follows a structured process encompassing problem identification, data collection, model development, rigorous Verification and Validation (V&V), experimental design, and analysis. V&V are critical steps, ensuring a model’s internal consistency (verification) and its accurate representation of the real system (validation).

Simulations are broadly classified into discrete-event models, where system states change at distinct points in time, and continuous models, where state variables change continuously. Key methodologies include Discrete-Event Simulation (often used for queuing systems), Continuous Simulation (based on differential equations), and Monte Carlo Simulation (a technique for numerical experiments using random sampling). While M&S provides significant advantages—such as testing systems without real-world risk, identifying bottlenecks, and exploring new policies—it also presents challenges, including the need for domain expertise, significant time and cost investment, and potential difficulty in translating results.