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Mansfield University... Developing Tomorrow's Leaders Mathematics - Course Goals, Objectives, and Outcomes



CIS / MA 3308 Operations Research


Course Goals: This course will develop a thorough understanding of linear programming and certain stochastic processes, the ability to solve linear programs and stochastic processes’ problems, and the ability model standard business problems and other problems using linear programs and stochastic processes.

Course Objectives:

  1. Develop linear programs from standard business problems
  2. Develop linear programs from other appropriate applications
  3. Illustrate how the simplex algorithm solves linear programs
  4. Develop the code for the simplex algorithm
  5. Illustrate how integer program algorithms solve integer programs
  6. Illustrate how duality theory solves linear programs
  7. Construct a project network and apply program evaluation review technique and critical path management
  8. Develop code to simulate observations from certain probability distributions
  9. Develop code to simulate the movement of a Markov chain
  10. Describe standard properties of Markov Chain and classify a Markov Chain according to them
  11. Derive the steady state solution and mean first passage matrix of a Markov chain
  12. Interpret a steady state solution and mean first passage matrix of a Markov chain
  13. Illustrate how Markov chains can solve standard business problems
  14. Illustrate how queuing theory can solve problems with inter-arrival and service times exponentially distributed using

Course Outcomes: Student will be able to

  1. convert standard business problems into linear programs
  2. convert problems from other appropriate applications into linear programs
  3. solve linear programs using the simplex algorithm
  4. code the simplex algorithm
  5. solve integer programs using integer program algorithms
  6. solve linear programs using duality theory
  7. construct a project network and apply program evaluation review technique and critical path management
  8. code the simulation of observations from certain probability distributions
  9. code the simulation of the movement of a Markov chain
  10. classify a Markov Chain according to standard properties
  11. calculate a steady state solution and mean first passage matrix of a Markov chain
  12. interpret a steady state solution and mean first passage matrix of a Markov chain
  13. solve standard business problems using Markov chains
  14. solve problems with inter-arrival and service times exponentially distributed using queuing theory