IE 630: Simulation Modelling and Analysis

Prerequisite:  Instructor's permission


Overview of basic concepts from probability and statistics concerning random variables, correlation,estimation, confidence intervals, hypothesis testing.

Fundamental concepts of System Simulation: Discrete event simulation, Monte Carlo simulation

Generation and testing of random numbers. Generation of random variates, random vectors,correlated random variates and stochastic processes. Input modeling; useful probability distributions;hypothesizing families of distributions, estimation of parameters, testing goodness of fit.

Building Monte Carlo / Discrete event simulation models of various processes and systems. Use ofgeneral purpose languages such as Python/R/C++ and/or packages such as Anylogic, Flexsim, etc forto build simulation models.

Simulation Output data analysis for a single system; statistical analyses for transient systems andsystems in statistical equilibrium. Comparing alternative system configurations; confidence intervals,ranking and selection. Variance reduction techniques. Experimental design, sensitivity analysis andsimulation-based optimization.

Optional Topics: Agent based simulation modeling and analysis; System Dynamics Modeling andanalysi

  • A. M. Law and W. D. Kelton (2000), Simulation Modeling and Analysis, 3rd Ed., McGraw HillInternational - 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.
  • G Gordon, (2015), System Simulation, Pearson