Course Level: Beginner
Format Requirements: This is a hands-on course that tries to balance theory with practice and will use in-class exercises to teach the building blocks of discrete-event simulation. Participants will also learn the basics of queuing theory and learn how to compare different simulation scenarios. Goals: 1) Understand basic queuing theory 2) Learn basic modeling techniques 3) Learn basic simulation statistics. Requirements: Participants will require a laptop computer running Windows 95 or higher. Participants may share laptops. Discrete-event simulation models will be constructed using Arena™. Other software may also be introduced. Demonstration versions of this software will be made available during and before the course. No previous knowledge is necessary though the textbook “Simulation with Arena” by Kelton and Sadowski and “Simulation Modeling and Analysis” by Law and Kelton are recommended.
Background: Discrete-event simulation (DES) is a method for modeling systems where competition for resources is an important feature, such as healthcare. DES has been successfully applied in industrial engineering and operations research since the 1960s. In DES, models approximate the same cause and effect relationships found in real, proposed or conceptual systems. This allows investigators to experiment using both sensitivity analysis and “what-if” experiments where the model structure and causal relationships are varied. DES is most useful when analyzing problems that involve resource constraints, competition for resources, interdependent events, or for understanding emergent behavior and illustrating and communicating complex processes to stakeholders. Basic concepts include entities, events, attributes, resources, queues and delays. Basic statistical concepts discussed will include the difference between observational, e.g.- waiting times, and time-persistent statistics e.g., utilization and queue length.
Description and Objectives:
- Understand basic queuing theory
- Learn basic modeling techniques
- Learn basic simulation statistics.