Lab 1&2. Simulation Basics

Lab 1. Introduction to Simulation

I. What is Simulation? 

1. A simulation model is an imitation of a process or system over time. 

2. These models are used to understand the system’s behavior and evaluate the potential effect of operational changes to the system. 

3. Real-world systems are often too complex to solve or represent analytically, making simulations a useful tool. 

 

II. When is Simulation Useful? 

1. To better understand the system by conceptualizing it. 

2. To evaluate the effect of new designs and policies to the system without altering the real operation. 

3. To train others. 

4. To show an animation of the system. 

5. To determine the capacity and resources needed. 

 

III. Disadvantages of Simulation

1. Building a simulation model can be time consuming and expensive. 

2. Results may be difficult to interpret. 

3. The realistic animation can be too persuasive and can lead to misinterpretation of the results. 

4. Simulations provide information, not solutions. 

 

IV. When is Simulation NOT ideal? 

1. Problems can be solved analytically or through common sense. 

2. Direct experiments on the system are easier to execute. 

3. Costs exceed savings. 

4. Not enough resources, time, or data are available. 

5. Management has unreasonable expectations. 

 

V. Simulation Application

1. Warehousing process improvement (Supply Chain) 

2. Amusement guest flow (Service) 

3. Automobile assembly line (Manufacturing)

4. Airport gate availability (Aviation)

5. Emergency Room wait time analysis (Healthcare) 

6. Evacuation plan evaluation (Government) 

 

VI. Types of Simulation

1. Static simulation: time-independent. (Monte Carlo simulation) 

ex) flipping a coin 100 times to determine heads vs tail proportion. 

2. Dynamic simulation: the changes in the system over time. 

ex) A production line model to evaluate its performance.

3. Deterministic simulation: no random variables

ex) A clinic with arrivals occurring at scheduled appointment times. 

4. Stochastic (Probabilistic) simulation has random variables to represent uncertainties in the system. 

ex) Arrivals at coffee shops.

5. Discrete (Discrete-event) simulation: only change at discrete time point where an even occurs that progresses the simulation. 

ex) The number of cars in a parking structure. 

6. Continuous simulation: changing continuously in time

ex) Water available at a reservoir. 

 

VII. Steps of a Simulation Project 

1. Problem Formulation

2. Objective Definition

3. Model Conceptualization

4. Data Collection

5. Run Simulation Experiments

6. Documentation and Reporting

7. Implementation and Insights

Lab 2. Elements of a System

I. Entities 

1. Object of interest in the system

2. Main items processed through the system 

3. Can be: 

1. Human / Animate (Customers in a supermarket)
2. Inanimate (Cars in a production line) 
3. Intangible (Phone calls in a call center) 
4. Continuous (Gas in a tank) 

II. Activities

1. Tasks performed in the system over a period of time

2. It can be directly or indirectly involved in the processing of entities. 

 

III. Events

Instantaneous occurrence that may change the state of the system. 

 

IV. Resources

1. Which activities are performed.

2. Can be: 

1. Human (Employees)
2. Inanimate (Machines)
3. Intangible (Energy) 

V. Locations (Stations) 

Specific places within the system where activities or events occur 

 

VI. Input: Decision Variables 

1. Define the properties of the system elements

2. ex) resource quantities, activity times, control logic 

 

VII. State Variables

1. Describe the status of the system at any point in time

2. How many entities left in the system 

3. ex) is a resource busy or idle? Number of entities in the queue, number of entities that have left the system 

 

VIII. Output: Response Variables

1. Measure the performance of the system: make critical decision 

2. ex) resource utilization: how equipment needs, average time of entity in the system 

 

IX. Example: Penny Cleaning Company 

1. Situation

1. There is a serial production line to clean and shine pennies. 
2. Dirty pennies come in from storage into a cleaning station.
3. Once they are cleaned, they are moved to a shining station.
4. After they are cleaned, pennies are stored and leave the system. 
5. The stations have one worker and can only clean one penny at a time. 

2. What are the events, activities, entities, locations, resources and state variables in this system? 

1. Events: 

• Penny arrives to the line (cleaning station queue)
• Penny arrives at the cleaning station
• Cleaning process is completed (Penny is moved to shining queue)
• Shining process is completed
• Penny leaves the line

1. Activities: Cleaning process, shining process
2. Entities: Penny
3. Locations: 

• Cleaning station queue
• Cleaning station 
• Shining station queue
• Shining station 
• Finished good storage

1. Resources: workers at stations
2. State variables: 

• Status of stations: busy / idle
• Number of pennies in the system
• Number of pennies waiting
• Number of finished pennies

 

X. Metrics: Throughput

1. Measures the number of entities processed in a system over time

2. It always includes a time unit

ex) “10 calls per shift”, “20 pennies per hour” 

 

XI. Metrics: Flow time

1. Total time to move an entity through the system 

2. ex) Penny Fab

1. Cleaning process takes 3.5 minutes
2. Shining process takes 2.5 minutes
3. We assume that pennies did not wait in queues at all
4. Then the flow time would be 6 minutes per penny. 

 

XII. Metrics: Work-In-Process (WIP) Inventory

1. The number of entities in the system that have not been completed. 

2. In general, the average WIP = average number of entities at stations + average number of entities in queues 

3. The system variability (arrival rate, processing times) can have a significant impact on this calculation.