STAT 346 Statistical Methods in Operations Research (3)

 Catalog Description:

Linear programming, simplex method, project management with PERT/CPM, deterministic dynamic programming, probabilistic dynamic programming, integer programming, queueing theory, introduction to SAS. (Offered fall semester, even years.)

 Prerequisites:

STAT 291 Statistics for Mathematical Sciences I

  1. Familiarity with matrix operations including inversion.
  2. Ability to perform Gauss-Jordan elimination.
  3. Familiarity with the beta, normal, exponential, and Poisson distributions, the Central Limit Theorem.
  4. Ability to determine the mean and variance of a distribution and a linear combination of independent random variables.
  5. Ability to use the computer for problem solving.
 Required Course Materials:

F. Hillier and G. Lieberman, Introduction to Operations Research, 9th edition, McGraw-Hill, 2010 (ISBN 0-07-337629-9)
Prerequisite course material: J. Devore, Probability and Statistics for Engineering and the Sciences, 8th edition, Cengage, 2012 (ISBN: 9780538733526)

 Course Coordinator:

L. Marlin Eby, Ph.D., Professor of Mathematics and Statistics

 Course Audience:
    1. Students majoring in mathematics or computer science or minoring in statistics.
    2. Students wanting a more rigorous decision science course.
    3. This course can be used to meet the elective requirement for both mathematics majors and computer science majors.
 Course Objectives:
    1. To intuitively understand each concept.
    2. To understand, when possible and appropriate, the rigor of a mathematical proof.
    3. To integrate topics by identifying commonalties.
    4. To understand the limitations of each analysis through consideration of assumptions.
    5. To express general concepts in terms of the application.
    6. To communicate results, clearly and completely, in a manner appropriate to nonquantitative audiences.
    7. To use the computer for analysis only after understanding how to perform the analysis manually.
 Topics:
    1. Introduction to SAS: overview.
    2. Introduction to Linear Programming: model, assumptions, geometric and algebraic solutions.
    3. Simplex Method: standard and nonstandard problems, post-optimality analyses.
    4. Project Management with PERT/CPM: overview, networks, project scheduling, and uncertain activity durations.
    5. Dynamic Programming: deterministic and probabilistic, tabular and graphical solutions.
    6. Integer Programming: exact solution and approximation methods.
    7. Queueing Theory: single and multiple server, single and multiple customer types, interarrival, waiting, and service time distributions.
 

Revised: August 2010