This course provides students with an introduction to an area of management science that is sometimes called quantitative methods or operations research. The objective of this course is to have students develop an appreciation of the management science approach to problem formulation and solution that is now so important in today’s business and industrial sectors. The course focuses on quantitative approaches to decision making and introduces a variety of management sciences models, methods, and procedures. The major areas of study are linear programming (LP), simulation modeling, and forecasting.
OPMT 1197 with a minimum grade of 65% or an equivalent college level Business Stats course with a minimum B grade, Accessibility to and basic knowledge of personal computers. The following calculator is required for this course: BA II Plus / Financial Calculator by Texas Instruments
$596.68 - $619.09 See individual course offerings below for actual costs.
No class on Monday, Feb 7th and Monday, April 13th
1 seat remaining as of Nov 21, 2019 2:53 am (PST). Availability may change at any time.
Upon successful completion of the course, the student will be able to:
Use graphical solution procedures for LP problems with only two variables to understand how LP problems are solved.
Understand how to setup LP problems in a spreadsheet and solve them using Excel Solver.
Model a wide variety of LP problems: Understand major business application areas for LP problems such as manufacturing, marketing, labour scheduling, blending, transportation, and multi-period planning.
Formulate Integer Programming (IP) models; setup and solve IP problems using Excel Solver.
Monte Carlo Simulation
Explain the advantages and disadvantages of simulation.
Tackle a variety of problems using Monte Carlo simulation, such as NPV/IRR models, expected profit models, and models for operational decisions and policies.
Setup and solve simulation models using Excel’s standard functions.
Understand and know when to use various families of forecasting models (qualitative models versus time series models).