Introduction to Monte Carlo and Discrete-Event Simulation

Learn the basics of Monte Carlo and discrete-event simulation, how to identify real-world problem types appropriate for simulation, and develop skills and intuition for applying Monte Carlo and discrete-event simulation techniques. Taught by Barry Lawson and Larry Leemis, each with extensive teaching and simulation modeling application experience. This intensive Hands-On course is developed by the largest organization of analytics professionals, INFORMS.


What You Will Learn

Participants will learn the basics of Monte Carlo and discrete-event simulation. Specifically, they will learn to identify real-world problem types appropriate for simulation, and will develop skills and intuition for applying Monte Carlo and discrete-event simulation techniques. Via hands-on interactive sessions, participants will investigate the use of Monte Carlo simulation in decision making, and the use of discrete-event simulation to solve mathematically intractable problems in stochastic modeling.

During the course, both open-source and state-of-the-art simulation software will be used, incorporating software written by the instructors. This is not a software training course, nor does the course focus on one particular simulation language. Rather, the course emphasizes the fundamental concepts of, and proper interpretation of results from, Monte Carlo and discrete-event simulation models.

By the end of the course, participants will:

  • be familiar with commonly-used techniques in simulation, such as random number and variate generation, input modeling, events and event types, run-length issues, autocorrelated output, and presentation of simulation results.
  • be able to identify problems from their specific domains suitable for simulation, and correctly approach the modeling of those problems, including identification of simulation goals and necessary real-world data.
  • be able to implement and execute Monte Carlo and discrete-event simulation models and correctly interpret and present the results.  

Course Outline

*Day One: Monte Carlo Simulation*

1) Making the Simulation Pitch

  • When to simulate
  • Knowing your intended audience
  • Visualizing statistical output
  • Simulation animation
  • Great advances in simulation

2) Monte Carlo Modeling

  • Deterministic models
  • Stochastic (random) models 
  • Random number and variate generation
  • Probabilistic models

3) Hands-on: Monte Carlo Simulation

  • Example: Single-period asset allocation
  • Identifying appropriate probabilistic models
  • Effects of randomness
  • Sensitivity analysis
  • Assessing goodness of estimates
  • Probabilistic convergence
  • Drawing conclusions from simulation results
  • Example: Retirement planning

*Day Two: Discrete-event Simulation*

1) Time-dependent Simulation

  • World views: process-oriented vs. next-event
  • Observational-based statistics
  • Time-based statistics
  • Event types
  • Sequencing of events
  • Understanding the time-advance mechanism

2) Real-world Applications of Queuing

  • Simulation goals & objectives
  • Three levels of model development
  • Input modeling
  • Stationary and non-stationary Poisson processes
  • Building a next-event simulation model
  • Verification & validation of a model
  • Implementation: open-source software
  • Designing simulation experiments
  • Examining run-length effects
  • Effect of initial conditions
  • Identifying and handling autocorrelated output
  • Implementation: commercial simulation software

3) Inventory Systems & Hybrid Systems

  • Goals & objectives
  • Identifying performance measures of interest
  • Implementation: open-source software  


Barry G. Lawson, University of Richmond Barry Lawson

Barry Lawson is Associate Professor of Computer Science in the Department of Mathematics and Computer Science at the University of Richmond. He received his Ph.D. in Computer Science from The College of William & Mary in 2002, and previously worked on flight simulation models in the Simulation Systems Branch laboratory at NASA Langley in Hampton, VA.

Barry has more than 15 years’ experience teaching various computer science courses at the undergraduate and graduate levels, including simulation courses at both levels. He is actively involved in NSF- and HHMI-funded curricular development in computer science and integrated sciences. His current research interests are focused in agent-based simulation, e.g., investigating antibiotic resistance within hospital wards. His research has also focused on various topics in simulation, including simulation of high-performance computing systems, and in computer security. He is a member of INFORMS, the Association of Computing Machinery (ACM) and the IEEE Computer Society.

More information about Barry can be found at

Lawrence M. Leemis, The College of William & Mary Larry Leemis

Larry Leemis is a Professor in the Department of Mathematics at The College of William & Mary. He received his PhD in Industrial Engineering from Purdue University in 1984, and has previously taught at The University of Oklahoma, Baylor University, and Purdue University. He has published five textbooks, written over 100 journal articles, book chapters, and proceedings papers, and won 12 teaching awards. He has consulted for over two dozen government, corporate, and non-profit entities, including Argonne National Laboratory, The United States Army, Navy, and Air Force, the FAA, NASA/Langley Research Center, Delco Electronics, and AT&T. Larry has also been an investigator on over two dozen research grants from a variety of funding agencies. His teaching and research interests are in reliability, discrete-event simulation, probability, statistics, and computational probability. He is a member of the American Statistical Association and INFORMS.

More information about Larry can be found at  



Members - $1,295

Non-members - $1,495


Members - $1,395

Non-members - $1,595

*Discounts available for 3 or more from the same organization.

Course Dates and Locations

Coming Soon


Cancellations - cancellations must be in writing and received 21 days or more prior to the start of the course. A refund will be issued less a $100 processing fee. Cancellations less than 21 days prior to the start of the course will not be eligible for a refund.

Substitutions - if you cannot attend you may send a substitute without incurring a fee provided notice is given in writing (please include substitutes' name) at least 72 hours prior to the start of the course.

Transfers - you may transfer to an earlier or later course date provided the request is received in writing at least 14 days prior to the start date of the course originally booked. There will be a $50 rebooking fee.