Hands-On System Simulation

Classify simulation models by characteristics such as deterministic versus stochastic, static versus dynamic, discrete versus continuous, open versus closed, and agent-based and system dynamics approaches, with real-world examples.

Explore popular simulation software such as Arena, Flexsim, Simulate, Analogic, Pro-model, Technometrics, and Witness to model discrete event processes with 3D visualization and cloud-based analysis.

Explore the binomial distribution as a discrete model for the number of successes in fixed independent trials with two outcomes and probability p, using the PMF and CDF.

Explore the jackknife technique, a leave-one-out resampling method to reduce bias and estimate variance, with practical uses in regression diagnostics, cross-validation, and small datasets.

Variables are placeholders for data that track statistics, control logic, and initial values in simulations. Name them descriptively, use real or string types, and define 1D or 2D structures.

Group resources, entities, and visuals with the set module in Rockwell Arena to simplify management and automate routing using resource sets and entity pictures.

Learn how Anaconda, Jupyter, and Visual Studio Code streamline Python development by managing environments with Conda, running interactive notebooks, and debugging and version control in one integrated workflow.

Explore data structures in Python, including lists, tuples, and sets, and learn how to store, access, modify, and operate on these collections.

Explore intermediate functions, including recursion, tail recursion, currying, partial functions, closures with state, decorators, and generators, with practical factorial examples.

Learn file handling in Python by using open, read, write, and close; work with CSV and pandas to load, manipulate, and export data.

Explore object oriented programming in Python, focusing on encapsulation, inheritance, and polymorphism to create classes and objects with reusable attributes and methods.

simulate airport check-in using python, modeling passenger interactions with counters, queues, and priority handling for business vs economy, and analyze load distribution and counter idle times.