BA 409

Operations Research

Mark Parker

T and Th, 9:30 - 10:45

SH 123 or Fortin 115 (Computer Lab)


Other Resources

Introduction

Operations Research is the study of quantitative techniques to help management make informed decisions about the daily operations of their organization. To maintain a competitive edge in a global economy, companies in diverse industries are investing in Operations Research, Management Science, and Decision Analysis. The goal of the course is to provide an introduction to the quantitative methods available for performing analyses and providing input to business decisions. Specifically, my goals are for you to learn to:

  1. recognize specific types of decision problems and modeling approaches to aid in their resolution,
  2. objectively evaluate quantitative models that you will be expected to interpret in the future,
  3. be able to construct and use basic models, and
  4. understand the strengths and weaknesses of these modeling approaches.

We'll attain these goals with a "hands-on" approach via Excel spreadsheet modeling, which is the tool of choice in the modern work environment. No prior knowledge of Excel is required; you will be taught the skills from class and the text, and gain proficiency by doing the homework problems.

Text

textbook Spreadsheet Modeling and Decision Analysis,
Fourth Edition
Cliff Ragsdale South-Western

Schedule

This schedule is tentative and may change as required.

Week Date Topic Text Chapters Suggested Homework Handouts
Week 1 Jan 10 Course Introduction Chapter 1 Block 1 Homework  
  Jan 12 Lab 1: Excel Basics   Block 1 HW Solns Lab 1 Handout
Lab 1 Excel
Week 2 Jan 17 Lab 2: Exponential and Poisson Distributions     Lab 2 Handout
Lab 2 Excel
  Jan 19 Introduction to Queues 13.0 - 13.6   Queuing Introduction
Q.xls
Week 3 Jan 24 Lab 3: Queuing Models in Excel 13.0 - 13.8   Lab 3 Handout
  Jan 26 Queuing Part II     Queuing Analysis
Week 4 Jan 31

Lab 4: M/G/1 and M/D/1 Queues

13.9 - 13.10   Lab 4 Handout
  Feb 2 Lab 5: Queueing Analysis     Lab 5 Handout
Week 5 Feb 7 Lab: Project 1 Work Day     Project 1 Handout
  Feb 9 Exam 1     Sample Exam 1
Week 6 Feb 14 Linear Programming 2.0 - 2.12 Block 2 Homework  
  Feb 16 Lab 6: LP In Excel 3.0 - 3.7 Block 2 HW Solns Lab 6 Handout
Wapiti Brewing.xls
Week 7 Feb 21 LP Models 3.9, 3.11   LP Modeling
  Feb 22 Sensitivity Analysis 4.0 - 4.6   Sensitivity Analysis
Sample Exam 2
Sample Exam 2 Solutions
Week 8 Feb 28 Lab 7: LP/IP/NLP Models 8.0 - 8.3

 

Lab 7 Handout
  Mar 2 Exam 2     Project 2 Handout
Week 9 Mar 14 Introduction to Forecasting 11.0 - 11.4 Block 3 Homework  
  Mar 16 Lab 8: Stationary Data 11.4 - 11.6 Block 3 HW Solns Lab 8 Handout
Time Series Examples.xls
Week 10 Mar 21 Lab 9: Regression 9.0 - 9.9   Lab 9 Handout
Regression Example.xls
More Regression.xls
  Mar 23 Regression and Multiple Regression 9.0 - 9.9
9.11 - 9.17


Regression Handout
Week 11 Mar 28 Non-Stationary Data
11.10 - 11.12
11.15 - 11.17
  Non-Stationary Handout
  Mar 30 Lab 10: Multiple Regression and Non-Stationary Data     Lab 10 Handout
multiple regression.xls
nonstationary templates.xls
data for trend lab.xls
Week 12 Apr 4 Seasonality 11.7 - 11.9
11.13 - 11.14
  Seasonality Handout
  Apr 6 Lab 11: Seasonality Part I     Lab 11 Handout
seasonality.xls
Week 13 Apr 11 Lab 12: Seasonality and Regression 11.18 - 11.20   Lab 12 Handout
Holt seasonality.xls
regression seasonality.xls
  Apr 13 Lab: Project 3     Project 3 Handout
Week 14 Apr 18 Decision Analysis Introduction 15.0 - 15.6 Block 4 Homework Decision Analysis Handout
  Apr 20 Probabilistic Methods 15.7 - 15.8 Block 4 HW Solns  
Week 15 Apr 25 Lab 13: Multicriteria Decision Making 15.16-15.17   Lab 13 Handout
Multicriteria scoring models.xls
  Apr 27 Lab: Project 3      
  May 2 Exam 3 - Tuesday: 3:00 - 4:45
    Sample Exam 3
Sample Exam 3 Solutions

Grading

Assignment Percentage
Labs 16%
Projects 30%
Examinations 54%

Your grade will translate to a letter grade as follows :


mark parker
Last modified: 27 April 2006