Modeling and Simulation Flashcards

Vocabulary flashcards based on lecture notes for exam preparation.

Simulation

The imitation of the operation of a real-world process or system over time.

Model

A simplification of a real system used to study its behavior.

Modeling

The process of representing a system with a specific tool to study its behavior.

Analytic Model

A model where a mathematical approach is feasible.

Simulation Model

A model used for complex systems.

Experimental Model

A model used when the real system already exists.

System

Any set of interrelated components acting together to achieve a common objective.

System Boundary

The defined limit of a system, separating it from its environment.

System Environment

The external surroundings and factors that can affect a system.

Entity

An object of interest in a system (e.g., machines in a factory).

Attribute

A property of an entity (e.g., speed, capacity).

Activity

Represents a time period of specified length within a system (e.g., welding).

State of a System

A collection of variables necessary to describe the system at any time.

Event

An immediate occurrence that might change the state of the system.

Endogenous Event

Activities and events occurring within a system.

Exogenous Event

Activities and events in the environment that affect the system.

Discrete System

A system where the state variable(s) change only at discrete points in time.

Continuous System

A system where the state variable(s) change continuously over time.

Hybrid System

A combination of continuous and discrete dynamic system behavior.

Physical System

Systems with variables that can be measured with physical devices.

Conceptual System

Systems where measurements are conceptual or imaginary and in qualitative form.

Esoteric System

Systems where measurements are not possible with physical measuring devices.

Independent System

A system where events have no effect upon one another.

Cascaded System

A system where the effects of events are unilateral (one-way).

Coupled System

A system where events mutually affect each other.

Static Simulation Model

Represents a system at a particular point in time (Monte Carlo simulation).

Dynamic Simulation Model

Represents systems as they change over time.

Deterministic Simulation

Simulation models that contain no random variables.

Stochastic Simulation

Simulation models that contain one or more random variables as inputs.

Verification

Ensuring the computer program performs properly and the model is built right.

Validation

Calibrating the model and ensuring that we built the right model.

Random Experiment

An experiment with known outcomes whose outcome cannot be predicted with certainty.

Sample Space

Set of ALL possible outcomes of a random experiment.

Discrete Sample Space

A sample space that consists of a finite or countable infinite set of outcomes.

Continuous Sample Space

A sample space that contains an interval of real numbers.

Event (Probability)

A result of none, one, or more outcomes in the sample space.

Conditional Probability

The probability of an event B given that event A has occurred, denoted as P(B|A).

Discrete Random Variable

A random variable with a finite (or countable infinite) range.

Probability Mass Function

Lists the probability that a discrete random variable takes on each of its possible values.

Cumulative Distribution Function

Measures the probability that a random variable assumes a value less than or equal to x.

Probability Density Function

For continuous random variable, that describes the relative likelihood for this random variable to take on a given value.

Queueing System

A system where entities arrive, wait in a queue, and are served.

Calling Population

The population of potential customers for a queueing system; can be finite or infinite.

Queue Discipline

The logical ordering of customers in a queue that determines which customer is served.

Inventory System

A system that manages and controls the stock levels of goods.

Random Number Generator (RNG)

Any mechanism that produces independent random numbers.

Pseudorandom Numbers (PRNs)

Numbers created by a random number generator that are calculated but appear random.

Uniformity

Random numbers must be independently drawn from a uniform distribution.

Independence

Each random number should be unpredictable from the previous number.

Linear Congruential Generators (LCG)

A common type of random number generator using a recursive formula.

Pseudorandom Numbers (PRNs)

Numbers created by a random number generator that are calculated but appear random.