Computer Simulation Exam

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22 Terms

1
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Define simulation modelling (LO1)

Simulation is a method to replicate a system with its dynamic processes in an experimental model to generate findings that can be transferred to the real world.

Simulation is the preparation, realization, and analysis of experiments to a model

2
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simulation modelling objectives (LO1)

  • Replicate system behavior

  • Analyze system performance under different conditions

  • Evaluate decision alternatives

  • Minimize costs, time, or risks

3
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simulation modelling applications (LO1)

  • time dynamics

  • complex systems

  • high cost or risk

  • uncertainty and variability

  • training

  • optimization and decision support

4
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Describe the three modelling methods in AnyLogic (LO2)

1. Discrete-Event Modelling (DEM):

  • Models the system as a series of events.

  • Example: Queueing system with machines and workers.

2. System Dynamics (SD):

  • Models stocks, flows, and feedback loops.

  • Example: Population growth, disease spread.

3. Agent-Based Modelling (ABM):

  • Models individual agents with behavior and interaction.

  • Example: People spreading a virus, customers shopping.

5
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mathematical and theoretical concepts behind simulation (LO3)

Core Concepts:

  • Queuing Theory: M/M/1, M/M/m models for waiting lines.

  • Randomness: Using random number generators (e.g. Mersenne Twister).

  • Probability Distributions: Normal, Poisson, Exponential, Triangular.

  • Key Metrics: Utilization, flow time, work-in-process, queue length.

  • System dynamics: Feedback loops, stocks and flows, non-linear behavior.

6
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Represent real-world processes in a simulation model (LO4)

  1. Define the real-world process (e.g., a production line).

  2. Identify components: sources, queues, servers, sinks.

  3. Assign probability distributions to uncertain variables.

  4. Use AnyLogic to build the model with appropriate logic and structure.

  5. Run simulations and adjust parameters to evaluate different scenarios.

7
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Combine operations management and logistics with simulation (LO5)

Examples:

  • Simulate warehouse operations and resource utilization.

  • Optimize delivery truck schedules.

  • Model assembly lines to reduce bottlenecks.

  • Use simulation to test inventory policies or capacity planning.

Benefits:

  • Reduces cost of real-life experimentation.

  • Supports strategic decisions in operations and supply chain design.

8
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Simulate supply chain procedures (LO6)

Capabilities:

  • Model multi-stage supply chains using AnyLogic.

  • Test strategies like Just-in-Time, safety stock, demand forecasting.

  • Include randomness in delivery times, processing times, and customer demand.

  • Measure KPIs like fill rate, lead time, and total supply chain cost.

9
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Have a basic knowledge of simulation modelling in general (LO7)

Key Points:

  • Simulation is ideal for complex, stochastic, and dynamic systems.

  • It complements or replaces analytical models when those fall short.

  • Important components: data collection, model validation, verification, scenario analysis.

  • Common model types: discrete-event, system dynamics, agent-based.

10
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What are the main objectives of simulation modelling?

To replicate system behavior, evaluate scenarios, predict outcomes, and support decision-making under uncertainty.

11
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Name three application areas of simulation modelling.

Logistics, healthcare systems, emergency response, manufacturing, marketing, training.

12
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What are the three modelling methods in AnyLogic?

Discrete-Event Modelling, System Dynamics, Agent-Based Modelling.

13
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What is Discrete-Event Modelling (DEM) used for?

Modeling event-driven systems like queues, manufacturing processes, or service systems.

14
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What does System Dynamics model?

Stocks, flows, and feedback loops in continuous systems such as population or epidemic models.

15
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What characterizes Agent-Based Modelling?

It focuses on individual entities (agents) and their interactions and behavior.

16
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What is queuing theory used for in simulation?

To analyze queue behavior: arrival rates, service rates, wait times, queue lengths, and system utilization.

17
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Name three important probability distributions used in simulation.

Exponential, Normal, Triangular (also: Poisson, Uniform).

18
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What is the purpose of using randomness in simulation?

To realistically model variability and uncertainty in real-world systems.

19
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How do you represent real-world processes in a simulation model?

Define system structure, assign distributions, use modules (e.g., source, queue, server), and run simulations in AnyLogic.

20
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How can simulation be applied in operations and logistics?

To optimize processes like production, transport, inventory, and workforce allocation.

21
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How can simulation improve supply chain performance?

By testing and analyzing strategies for inventory management, demand variability, lead times, and resource use.

22
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Why use simulation instead of analytical models?

Simulation handles dynamic, non-linear, and uncertain systems where analytical models fail.