Modeling and Simulation Course
This 3-day simulation course is designed for operations research analysts, systems and industrial engineers, military planners, computer scientists, and technical managers who would like to use simulation to design and optimize real-world systems. It encompasses a full spectrum of applications, including defense, manufacturing, healthcare, transportation, supply chains, communications networks, process reengineering, call centers, services, mining, inventory control, and financial analyses. The course presents definitive methods for developing a simulation model, ensuring its validity, choosing simulation software, selecting input-probability distributions, designing and analyzing simulation experiments, and project management. A case study illustrates the step-by-step application of simulation-modeling techniques. Students will have an opportunity to analyze simulation input and output data in class using ExpertFit and Excel. The prerequisite for this seminar is a course on probability and statistics, or the equivalent.
This is one of the world’s most-successful continuing education courses, having been presented more than 500 times in 20 countries on 6 continents.
Outline
Each Attendee Will Receive the Following:
- The book Simulation Modeling and Analysis (6th Edition, McGraw-Hill, 2024) by Averill M. Law – widely considered to be the “bible” of simulation with more than 172,000 copies sold and 26,600 citations
- A certificate of completion that is personally signed by the instructor
What You Will Learn (look to the right for more detail):
- Designing and Optimizing Systems via Simulation Modeling
- Selecting Simulation Software
- Building Valid, Credible, and Appropriately Detailed Simulation Models
- Modeling Randomness in Real-World Systems
- Designing and Analyzing Simulation Experiments
- Case Study
- 23 Critical Pitfalls in Simulation Modeling and How to Avoid Them
Critical Questions That the Seminar Will Answer:
- What is a definitive overall approach for conducting a simulation study?
- What are the most-important features to consider when selecting a simulation package?
- Why is a knowledge of probability and statistics necessary for performing successful simulation studies?
- How do you decide on an appropriate level of model detail?
- What are the most-important techniques for ensuring model validity and credibility?
- How do you select a probability distribution to represent a source of system randomness (e.g., a task time) when corresponding data exist?
- What probability distribution is often used to represent a source of randomness when no system data are available?
- Why is the normal distribution rarely appropriate for use in a (discrete-event) simulation model?
- How do you correctly model customers arriving to a system when their arrival rate varies as a function of time ?
- Why does a random-number generator need to be reproducible and have multiple independent streams?
- How can you determine the correct length of a simulation run, the required number of independent model replications, and the length of a warmup period (if steady-state performance measures are of interest)?
- How is a confidence interval based on the method of independent model replications used to determine the statistical precision of an estimated system performance measure?
- Why is it critical to interact with the decision-maker on a regular basis?
- Why is it necessary to document all model concepts, assumptions, algorithms, input parameters, and performance measures in what is called an "assumptions document"?
- How is the assumptions document validated?
- What are 23 critical pitfalls that can sabotage your simulation project and how can they be avoided?