Simulation Lec#12 (Discrete Simulation Models, Model Times).pdf

Discrete Simulation Model, Model Time
MODELING & SMINULATION

Modeling and Simulation: Course Outline
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Discrete Simulation Models
Understanding System Dynamics
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• Discrete simulation models are precious tools used in various fields to
understand and analyze the behavior of complex systems.
• Unlike continuous simulation models, which represent systems where
changes occur continuously over time, discrete simulation models
focus on systems where events occur at distinct points in time,
leading to discrete changes in system state.

Discrete Simulation Models cont…
Understanding System Dynamics
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• These models are highly versatile and can be applied to a wide range
of domains, including manufacturing, transportation, healthcare,
finance, and more.

Discrete Simulation Models cont… Key
Components
• Entities: Represent objects, individuals, or components within the
system being modeled.
• Entities interact with each other and the environment, leading to
changes in system state.
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Components
• Events: Represent specific occurrences or actions that affect the state
of the system.
• Events trigger changes in the system's behavior or state, such as
entity arrivals, departures, resource allocations, or state transitions.
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Components
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• Simulation Clock: Dictates the progression of time in the simulation.
• The simulation clock determines when events occur and in what
sequence, allowing analysts to study system behavior over time.

Components
• Simulation Engine: Executes the simulation by processing events in
chronological order and updating the state of the system accordingly.
• The simulation engine maintains a schedule of pending events and
advances the simulation clock to the next event time.
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Advantages
• Flexibility: Discrete simulation models can capture a wide range of
system behaviors, including complex interactions, variability, and
uncertainty.
• Granularity: These models allow analysts to study system dynamics at
a fine level of detail, enabling insights into individual events and their
impact on system behavior.
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Advantages
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• Scenario Analysis: Discrete simulation models facilitate the
evaluation of different scenarios, parameter values, or interventions,
helping analysts understand the consequences of various decisions or
policies.

Advantages
• Performance Evaluation: These models enable the assessment of
system performance metrics, such as throughput, resource utilization,
waiting times, and service levels, providing valuable insights for
system optimization and improvement.
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Discrete Simulation Models cont… Model
Time
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• Model time refers to the representation of time within a simulation model.
• In discrete simulation models, time is typically represented as a sequence
of discrete time steps, where each step corresponds to the occurrence of
an event or the completion of a process.
• The simulation clock governs the progression of time, advancing from one
time step to the next as events occur.

Time: Key Aspects
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• Event Scheduling: Events are scheduled to occur at specific points in
model time, based on predetermined event times or conditions.
• The simulation engine maintains a schedule of pending events and
processes them in chronological order.

Time: Key Aspects
• Time Advancement: The simulation clock determines when the next
event will occur, based on the current state of the system and the
scheduled event times.
• Time advancement may be deterministic or stochastic, depending on
the nature of the events and the simulation environment.
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Time: Key Aspects
• Simulation Horizon: The simulation horizon defines the duration of
the simulation, specifying the maximum amount of model time to be
simulated.
• Analysts may choose the simulation horizon based on the objectives
of the simulation and the desired level of detail.
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Time: Key Aspects
• Time Units: Model time may be expressed in various units, such as
seconds, minutes, hours, days, or other relevant time intervals,
depending on the application domain and the granularity of the
simulation.
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Time: Applications of Model Time
• Dynamic System Behavior: Model time enables analysts to study the
dynamic behavior of systems over time, capturing the sequence of
events and their effects on system state.
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• Performance Analysis: By tracking the progression of time, analysts
can evaluate system performance metrics, such as throughput,
response times, and resource utilization, providing insights into
system efficiency and effectiveness.
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• Scenario Comparison: Model time facilitates the comparison of
different scenarios or policies by simulating the behavior of the
system under varying conditions and observing the effects on system
performance and outcomes.

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• Discrete simulation models and model time are essential concepts in
the field of simulation modeling, enabling analysts to study the
behavior of complex systems over time.

Simulation Lec#12 (Discrete Simulation Models, Model Times).pdf

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