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CSA 471/571 Simulation (3 credits)

 

Typically offered during both the fall and spring semesters.

Catalog description:

Use of digital computer program to simulate operating characteristics of stochastic dynamic system. Topics: problems encountered in construction of simulation programs, random number generation, random variant sampling, programming in simulation and compiler languages, problems in design of successful simulation investigations, design of simulation experiments, interpretations of simulated output, and verification and validation. Case studies and projects used.

 

Prerequisites:

Probability and Statistics, CSA174 / CSA603 or equivalent, and CSA372 or equivalent.

 

Miami Plan:

MPT - Third course in thematic sequence, CSA 3 - Mathematical & Computer Modeling .

 

Objectives:

  • Compare and contrast simulation with analytic modeling approaches.
  • Formulate problems in a manner that leads to quantitative analysis.
  • Write simulation programs in a general-purpose language.
  • Write simulation programs in the high-level simulation language Arena.
  • Analyze input data for a simulation model and choose an appropriate probability distribution.
  • Design and conduct test studies to verify and validate a simulation model.
  • Use statistical analysis to analyze the output of a simulation model to answer questions and make decisions.
  • Conduct a complete simulation study. 

Learning Outcomes:

CSA471.1:  To be able to apply previous knowledge of probability theory to construct stochastic models of random systems.
CSA471.1.1  The student can use the probability theory to compute event probabilities, expected values, and variances in random environments.
CSA472.1.2  The student can use empirical and theoretical distributions to represent random components of a system.
CSA471.2:  To be able to write simulation programs to model systems.

CSA471.2.1  The student can write discrete event simulation programs in a general purpose programming language.
CSA471.2.2  The student can write simulation programs in a special purpose simulation language.
CSA471.2.3  The student can use pseudorandom number generators to generate random variables from empirical and theoretical probability distributions.
CSA471.2.4  The student can describe the operation of pseudorandom number generators and perform some elementary random number testing.

CSA471.3:  To be able to apply previous knowledge of statistics to analyze data representing the inputs to and outputs from a simulation model.
CSA471.3.1  The student can collect and analyze input data on a random component of a system and use appropriate estimators and statistical tests to fit this data to known theoretical distributions or use empirical distributions.
CSA471.3.2  The student can design a simulation program to collect output data on a wide variety of performance measures.
CSA471.3.3  The student can analyze output data from a simulation experiment using batch means and replication methods.
CSA471.3.4  The student can compute confidence intervals on system performance measures and perform mean comparison tests.

CSA471.4:  To enhance the student’s ability to use quantitative models in decision making.
CSA471.4.1  The student can create simulation models for a wide variety of application areas.
CSA471.4.2  The student can explain differences between simulation and analytic modeling approaches.
CSA471.4.3  The student is able to formulate a decision problem in terms of controllable variables, modeling assumptions, constraints, and objectives.
CSA471.4.4  The student can explain model verification and validation.
CSA471.4.5  The student can explain how the components of this course go together to conduct a complete simulation study.

Required topics (approximate weeks allocated):

  • Introduction to simulation and comparison with analytic models (1)
  • Problem formulation (includes presentation on entities, events, activities) (1)
  • Developing simulation models with different world views (.5)
  • Model verification and validation (.5)
  • Introduce basic concepts of a general purpose simulation language (2)
  • Data collection and analysis (2)
    • data analysis using descriptive statistics
    • data analysis using inferential statistics-Goodness of Fit tests
      • Chi-square test
      • Kolmogorov-Smirnov test
    • theoretical discrete distributions
    • theoretical continuous distributions
    • parameter estimation of theoretical distributions
  • Random numbers and random variate generation (2)
    • generating random numbers - linear congruential method
    • statistical tests of pseudorandom numbers
    • methods of generating random variates
      • inverse method
      • acceptance/rejection method
    • generating discrete random variates
    • generating continuous random variates
  • Advanced features of a general purpose simulation language (2)
  • Output analysis of a single system (1)
  • Comparing output of two systems (1)
  • Introduce basic concepts of simulators or parameter-driven simulation environments (1)
  • Exams/Review (1)

Graduate students:

Students enrolled in CSA 571 will be given additional readings and/or assignments