In the field of Operations Research, decision-makers are often confronted with complex problems that require careful analysis to determine the most effective course of action. Optimization techniques, such as linear programming, integer programming, and dynamic programming, are fundamental tools used to identify the best possible solution under a given set of constraints. These methods rely on well-defined mathematical models and are highly effective when the relationships between variables, constraints, and objectives are clear, quantifiable, and linear. Optimization techniques are preferred because they provide precise solutions, ensure resource efficiency, and allow for systematic decision-making in areas like production planning, transportation, inventory management, and workforce scheduling. However, real-world problems frequently involve complexities that limit the practical applicability of conventional optimization methods. Such complexities include non-linear relationships between variables, stochastic or random elements, interdependent constraints, and highly dynamic systems that change over time. For example, in a manufacturing system, machine breakdowns, variable processing times, and unpredictable demand patterns create uncertainties that cannot be easily incorporated into a linear or deterministic optimization model. Similarly, in supply chain management, factors like fluctuating transportation costs, lead time variability, and customer behavior introduce randomness and dependencies that render traditional optimization methods less effective or computationally infeasible. When an optimization technique becomes difficult or impractical to apply due to such complexities, simulation emerges as a powerful alternative. Simulation involves constructing a model that mimics the behavior of the real system, often incorporating randomness, time-dependent interactions, and complex interrelationships between variables. Unlike optimization, which seeks a single “best” solution, simulation evaluates the performance of a system under various scenarios, allowing decision-makers to observe potential outcomes, assess risks, and test different policies without implementing them in reality.