Stochastic simulations require a reliable stream of randomness to mimic real-world unpredictability. Pseudo-Random Number Generators (PRNGs)
: Contain probabilistic elements. Inputs are random variables, meaning outputs are statistical distributions. Example: Airport check-in line wait times. Continuous vs. Discrete-Event Models
: A collection of interacting entities acting together toward a specific objective. modeling and simulation lecture notes ppt top
When models are too vast for a single machine, distributed simulation splits the workload across multiple computers.
The choice of methodology depends on whether the system state changes continuously or at specific points in time: Example: Airport check-in line wait times
: A variable giving the current value of simulated time.
: A collection of entities (e.g., people, machines, data packets) that act and interact together toward a specific logical end. When models are too vast for a single
: Represents a system as it changes over time (e.g., conveyor belts). Deterministic vs. Stochastic Models
is the act of operating the model over time. It allows us to observe how a system behaves under various conditions without risking the actual system. 2. Key Types of Simulation Models