Techniques like Linear Congruential Generators (LCG). Random Variate Generation: Inverse Transform Techniques. B. Discrete-Event System Simulation (DES)
The industry standard for continuous and control system modeling.
: Planning test scenarios, runs, and sensitivity analysis.
Code debugging, structured walkthroughs, tracking entity pathways step-by-step, and performing balance checks (e.g., ensuring total entities entered equals total entities exited plus entities currently inside). 5.2 Validation (Building the Right Model) modeling and simulation lecture notes ppt top
A word problem: "A coffee shop has 1 barista. Customers arrive every 3 minutes (exponential). Service takes 2.5 minutes (normal, stdev 0.5). Simulate 8 hours. Find: (a) Average queue length, (b) Barista utilization, (c) Probability of waiting > 5 min."
: A simplified, abstract representation of a real-world system designed to study its behavior.
Slide 17 — Calibration and Parameter Estimation Techniques like Linear Congruential Generators (LCG)
Slide 12 — Modeling Techniques: Agent-Based Simulation
Unlike DES, which focuses on system processes, ABM focuses on individual autonomous entities ("agents"). Each agent follows its own set of rules, interacts with other agents, and adapts within an environment.
: The system runs for a fixed, clearly defined time window (e.g., a bank operating from 9:00 AM to 5:00 PM). Initialization bias heavily impacts results. : Historical data comparison
: Historical data comparison, Turing tests (expert assessments), and statistical hypothesis testing (e.g., Paired t-tests on model outputs versus real-world data). 8. Output Analysis and Variance Reduction
The simplest approach. It projects the next state using the current derivative slope. It is computationally cheap but highly susceptible to error accumulation if is too large.
: Drive the simulation using past historical inputs and compare outputs to past historical data.