Simulation Lecture Notes Review - Q&A

Flashcards based on lecture notes about simulation, covering topics from basic definitions to advanced concepts like queueing theory and statistical analysis of simulation outputs.

What is simulation?

A model that attempts to imitate a process or system.

What are the types of simulation?

Physical or mathematical, static or dynamic, deterministic or stochastic, and discrete or continuous.

What are the advantages and disadvantages of simulation?

Study complex systems, observe the effects of changes, recommend improvements, provide insights on variable interactions, and verify analytic solutions. Disadvantages include complexity and the need for expertise to implement and interpret correctly.

When is the appropriate use of simulation?

When studying a complex system, when changes can be simulated, when it can provide knowledge for improvements, when it can provide insights on variables, when used as a teaching method, to experiment with new policies or designs, and to verify analytic solutions.

What are the components of a system?

System, entity, attribute, activity, event, and state variable.

What is the first step in a simulation study?

Define the problem to be studied in the simulation.

What is the second step in a simulation study?

Clarify what the simulation is intended to achieve.

What is the third step in a simulation study?

Create a simplified logical model that reflects the real-world system, including assumptions.

What is the fourth step in a simulation study?

Gather real-world data required to drive the simulation inputs.

What is the fifth step in a simulation study?

Implement the conceptual model in simulation software.

What is the sixth step in a simulation study?

Ensure the simulation behaves as intended logically.

What is the seventh step in a simulation study?

Compare simulation output with actual system performance to check model accuracy.

What is the eighth step in a simulation study?

Plan scenarios or parameter changes to test in the simulation.

What is the ninth step in a simulation study?

Execute the simulation multiple times and analyze the results statistically.

What is the tenth step in a simulation study?

Perform additional simulations if results are inconclusive or further refinement is required.

What is the eleventh step in a simulation study?

Document model assumptions, logic, results, and interpretations.

What is the twelfth step in a simulation study?

Use the simulation insights to make real-world decisions or changes.

What is the difference between Monte Carlo and Discrete Event Simulation?

Monte Carlo uses probabilistic sampling for static problems, while Discrete Event Simulation uses event-driven progression for dynamic systems.

What are ERG basics?

A visual depiction modeling the association(s) between events in a simulation.

In ERG notation, what do Node A and Node B represent?

Events, not locations.

What are manual simulation techniques?

Event calendars.

What is simultaneity of simulations?

Events can occur at the same time during a simulation.

What is event calendar understanding?

Event calendars track events, time, and system state.

What is event calendar creation and updating?

Event calendars are created and updated by tracking events and changes in system state over time.

What are queueing terms?

Inter-arrival time, service time, waiting time, queue length, etc.

What is Kendall’s notation?

Used to classify queueing systems (e.g., M/M/1, M/G/1).

What are measures of performance?

Average delay in queue, average time in system, average number of customers in queue/system, and server utilization.

What is Little’s formula?

Relates entity-based averages to temporal averages.

What are steady state calculations?

Determine long-run average behavior of queueing systems.

What are the types of distributions?

Trace-driven, empirical, parametric, and non-parametric.

What are common parametric distributions?

Poisson, Binomial, Negative Binomial, Discrete Uniform, Exponential.

What are random number properties and their generation?

Should appear statistically independent and uniformly distributed. pseudo random numbers are generated with closed form mathematics, not truly random, but repeatable.

What is the inverse transform method?

Used to generate random variables from a desired distribution by "inverting" the cumulative distribution function (CDF).

What is distribution fitting?

Selecting a distribution and estimating its parameters to match observed data.

What is acceptance/rejection sampling?

A method for generating random variables X from U when the CDFs is hard (or impossible) to invert.

What is input modeling?

Selecting appropriate probability distributions for system inputs.

What is random variate generation?

Mechanism to generate random values based on the input distributions.

What is validation?

Ensuring the model's results are close enough to observed real-world data.

What is output analysis?

Interpreting simulation results to inform decision-making.

What is input uncertainty?

Errors due to incorrect or imprecise input distributions.

What is modeling error?

Oversimplification or inaccuracies in the model logic.

What is estimation error?

Arises due to finite sample size; addressed using confidence intervals.

What is the Weak Law of Large Numbers (WLLN)?

Sample mean converges in probability to the true mean.

What is the Strong Law of Large Numbers (SLLN)?

Sample mean converges almost surely to the true mean.

What is the Central Limit Theorem (CLT)?

Standardized sample mean approaches a normal distribution as n increases.

What are independent replications?

Multiple replications of the simulation using independent random number seeds, where each replication is i.i.d. (independent and identically distributed).

What are confidence intervals?

Used to estimate the error in the sample mean.

What are prediction intervals?

Used to estimate the range where a new replication's outcome will fall.

What is independent sampling?

Uses separate random number streams for each system being simulated, leading to larger variance.

What is correlated sampling?

Also known as Common Random Numbers (CRN), uses the same random numbers across systems to reduce variance.

What is model output comparison?

Used to determine whether differences in outputs between models are statistically significant, typically using a two-sample t-test.