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IE303: Simulation

Prerequisites topics
Probability & Statistics
Some knowledge of programming (any language; use of computers)

Contents:

  • Introduction to various types of simulation of dynamics and/or stochastic systems, including monte carlo methods, discrete event simulation, agent-baed modeling, system dynamics approach.
  • Understanding Steps in a Simulation study: model conceptualisation & scoping, input data collection, building simulation model, model verification and validation, design and conduct experiments, making valid observations from simulation results.
  • Fundamental concepts of System Simulation: Monte Carlo simulation, Discrete event simulation systems in presence or absence of uncertainty; Generation & testing of random numbers. Generation of random variates.
  • Input modeling; Fitting probability distributions; hypothesizing families of distributions, estimation of parameters, testing goodness of fit. Using software to analyze input data.
  • Building Monte Carlo / Discrete event simulation models of various processes and systems. Use of general purpose languages such as Python/R/C++ (for MCS) and/or packages such as Anylogic, Flexsim, etc (for DES) to build simulation models.
  • Simulation Output data analysis for a single system; statistical analyses for transient systems and systems in statistical equilibrium. Comparing alternative system configurations; confidence intervals, Using software to analyze simulation results.
  • Model verification and validation.

Advanced topics may include: Ranking and selection, Variance reduction techniques. Experimental design, sensitivity analysis and simulation-based optimization; Agent based modeling, System Dynamic methodology.

References

  • A. M. Law and W. D. Kelton (2000), Simulation Modeling and Analysis, 3rd Ed., McGraw Hill International - Industrial Engg. Series.
  • J. Banks, J. S. Carson, B. L. Nelson and D. M. Nicol (2013), Discrete Event System Simulation,6th Ed., Pearson Education International Series.
  • S. Ross (2012) Simulation​, Academic Press.
  • U Wilensky and W Rand (2015), An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo, MIT Press