8 Questions
Match the following terms with their definitions:
Sample space = Represents all possible outcomes for a random variable Stochastic model = Uses one or more random variables as inputs to the simulation System state = Collection of state variables needed to describe the system at a specific time Conceptual Model = Focuses on how comprehensive the model should be and identifies dynamic state variables
Match the following terms with their descriptions:
Deterministic Model = Simulation inputs consist of known values with no random variables Dynamic model = Represents a system as it evolves over time, like a conveyor system in a factory Discrete system = State variables change only at specific points in time, like in a bank example Continues system = State variables change continuously over time, as seen in the head of water behind a dam
Match the following terms with their characteristics:
Events = Instantaneous occurrences that can alter the system state without taking any time Mathematical model = Represents the system using logical and arithmetic relationships Simulation clock = Variable indicating the current simulated time value Validation = Ensures that the computational model aligns with the system being analyzed
Match the customer behavior with their description:
Balking = Customers decide not to join the queue Renege = Customers leave the queue after joining Jockeying = Customers switch between different queues Queue discipline = Determines order of service for customers
Match the arrival process with its description:
Infinite-population models = Characterized by interarrival times of customers Scheduled arrivals = Customers arrive at specific times Random arrivals = Customers arrive at unpredictable times Interarrival times = Usually follow a probability distribution
Match the simulation feature with its purpose:
Operating characteristic estimates = Obtained in less time than real system Time compression = Simulation models reduce time for estimates Queue behavior simulation = Models customer actions in queues Arrival process simulation = Models customer arrival patterns
Match the type of modeling with its characteristic:
Queue behavior modeling = Focuses on actions of customers in queues Time compression modeling = Used to speed up operating characteristic estimates Infinite-population modeling = Considers arrival patterns without population limit Random arrival modeling = Models customer arrivals at unpredictable times
Match the system component with its definition:
Queue discipline = Orders customers for service when server is available Arrival process = Characterizes how customers arrive in the system Time compression simulation = Reduces time needed to estimate operating characteristics Interarrival times = Time between successive customer arrivals
Test your knowledge on concepts related to random variable models, including sample space, events, deterministic models, and stochastic models.
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