PhD Qualifying Exam Information for Simulation

Materials Permitted:

  • Hard copy (typed or hand written) notes covering the topics below and created by the student taking the exam.
  • Laptop computer for model building use only
  • Calculator

1. Modeling Randomness

  1. Be able to describe what a random number seed is and how to generate random numbers from different streams.
  2. Given a probability distribution, be able to specify an algorithm for generating random variates from the distribution via the inverse transform method, convolution, mixtures, truncated, shifted, acceptance/rejection methods as deemed appropriate.
  3. Be able to describe the construction of P-P, and Q-Q Plots and be able to interpret their meaning within the context of input modeling
  4. Be able to perform an input distribution fitting exercise and make an input model recommendation, justified by statistical analysis

2. Statistical Analysis

  1. Be able to compute and interpret the meaning of confidence intervals
  2. Be able to compute the sample size necessary to estimate a desired output statistic to within +/- desired half-width.
  3. Be able to determine the overall confidence level associated with making a decision based on a set of confidence intervals
  4. Be able to set the individual confidence interval levels in order to ensure an overall confidence level on a decision based on a set of confidence intervals
  5. Be able to perform and interpret a basic multiple comparison analysis
  6. Be able to describe, discuss, and interpret a Welch plot to identify a possible warm up period for an infinite horizon steady state simulation
  7. Be able to set up an experimental design to perform a sensitivity analysis of a simulation according to desirable DOE criteria.
  8. Be able to interpret the results of a main effect experimental design analysis of a simulation.

3. Be able to build a model of a static (Monte Carlo) simulation situation and perform a statistical analysis of the results.

4. Be able to model discrete event dynamic systems commonly found within industrial engineering

  1. Be able to describe a system in terms of its transient, structural, attributes, resources, activities, events, processes, state variables, etc.
  2. Be able to represent the system within an object-oriented event-scheduling paradigm
  3. Be able to verify and validate a DEDS model
  4. Be able to exercise and interpret the results of a DEDS