Simulation Lecture Notes Review

Flashcards covering general simulation concepts, discrete event simulation, system dynamics, Monte Carlo simulation, verification, validation, experimentation, analysis, and practical examples.

Simulation

An abstract representation of real-world systems used to analyze behavior, predict outcomes, and identify improvements without conducting real-world experiments.

Benefits of Simulation

1. Cost and risk reduction 2. Analysis of complex situations 3. Strategic decision-making and scenario analysis

DES Components

1. Entities 2. Attributes 3. Events 4. Activities 5. Queues and Resources

Entities (DES)

Objects flowing through a system. Examples: customers, patients.

Events vs. Activities

Events: Instantaneous, system state changes. Activities: Actions requiring resources and duration.

Queue Disciplines in DES

1. FIFO: First-In-First-Out 2. LIFO: Last-In-First-Out 3. SIRO: Serve-In-Random-Order

DES Application Examples

Hospitals, banks, restaurants.

System Dynamics

Method analyzing complex systems through stocks, flows, and feedback loops to understand aggregate behavior over time.

Stocks and Flows

Stocks: Accumulations or system states (e.g., inventory). Flows: Rates of change affecting stocks (e.g., sales rate).

Feedback Loops

1. Reinforcing loops (growth/amplification) 2. Balancing loops (stabilizing effects)

Causal Loop Diagram

To visually represent feedback relationships in systems, highlighting reinforcing and balancing loops.

SD Application Examples

Workforce management, epidemic modeling.

Monte Carlo Simulation

A simulation method using random sampling and statistical analysis to evaluate outcomes under uncertainty.

Monte Carlo Applications

Inventory control, financial risk analysis.

DES vs SD (Time Representation)

DES: Discrete, event-based. SD: Continuous, aggregated over time

DES vs SD (Complexity)

DES: Detailed, operational. SD: Strategic, aggregate-level

Verification vs Validation

Verification: Checks correctness of model construction. Validation: Ensures model accurately represents reality.

Validation Techniques

1. White-box (internal structure) 2. Black-box (input-output accuracy)

Confidence Interval (Formula)

CI=xˉ±1.96sn

Sensitivity Analysis (Purpose)

To test how sensitive outcomes are to variations in parameters, ensuring robustness of results.

Example Entities (Bank Simulation)

Customers (entities), cashiers (resources), queues.

Example Stock-Flow (Bricklaying Workforce)

Stocks: Apprentices, qualified bricklayers, master bricklayers. Flows: Training, promotion, attrition rates

Managerial Insights from Simulation

Resource allocation, Staffing optimization, Process improvements

Activity Cycle Diagram (ACD)

Visual representation of sequences and cycles of activities, resources, and queues in DES models.

Causal Loop Diagram (Components)

Variables, causal links (+/-), feedback loops (Reinforcing/Balancing).

Probability Distributions (Examples in DES)

Exponential: customer arrivals. Normal: processing times. Uniform: service duration estimates