Computer Simulation Exam

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

simulation modelling objectives (LO1)

• Replicate system behavior

• Analyze system performance under different conditions

• Evaluate decision alternatives

• Minimize costs, time, or risks

simulation modelling applications (LO1)

• time dynamics

• complex systems

• high cost or risk

• uncertainty and variability

• training

• optimization and decision support

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.

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.

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.

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.

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.

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.

What are the main objectives of simulation modelling?

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

Name three application areas of simulation modelling.

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

What are the three modelling methods in AnyLogic?

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

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

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

What does System Dynamics model?

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

What characterizes Agent-Based Modelling?

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

What is queuing theory used for in simulation?

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

Name three important probability distributions used in simulation.

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

What is the purpose of using randomness in simulation?

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

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.

How can simulation be applied in operations and logistics?

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

How can simulation improve supply chain performance?

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

Why use simulation instead of analytical models?

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