vcc.ca
MATH 2230: Introduction to Operations Research with Excel
Effective date
January 2026
Department
UT Math
School
Arts and Sciences
Description
This course introduces students to the fundamental concepts and techniques of Operations Research (OR), with a strong focus on practical applications using Microsoft Excel. Emphasizing linear programming, optimization, and decision modeling, students will learn to formulate real-world problems mathematically, solve them using standard techniques, and interpret results. Through hands-on exercises and case studies, students will apply industry-relevant tools such as Excel Solver to conduct sensitivity analysis, simulation, and resource allocation.
Credits
3.0
Year of study
2nd Year Post-secondary
Prerequisites
MATH 1221 or equivalent (taken prior to or concurrently).
Corequisites
None
Course Learning Outcomes
Upon successful completion of this course, students will be able to:
- Apply fundamental concepts of Operations Research to solve practical problems using Excel.
- Develop linear programming models and solve optimization problems using Excel Solver.
- Conduct sensitivity analysis and evaluate the impact of variable changes on Operations Research models.
- Solve transportation, assignment, and shortest path problems using network models.
- Apply simulation techniques, including Monte Carlo methods, to analyze complex systems.
- Interpret queueing theory models and apply them to practical business scenarios.
- Work collaboratively on case studies and group projects to solve real-world Operations Research problems.
- Effectively communicate solutions and insights through written reports and presentations.
- Analyze ethical considerations and future trends in Operations Research.
- Enhance critical thinking and problem-solving skills through Excel-based modeling.
Prior Learning Assessment & Recognition (PLAR)
None
Hours
Lecture, Online, Seminar, Tutorial: 45
Clinical, Lab, Rehearsal, Shop, Kitchen, Simulation, Studio: 15
Total Hours: 60
Clinical, Lab, Rehearsal, Shop, Kitchen, Simulation, Studio: 15
Total Hours: 60
Instructional Strategies
Lectures: Interactive sessions with coding demonstrations.
Lab Work: session with hands-on exercises to apply data science concepts in R and SQL.
Problem-Based Learning: Engage students with practical, data-focused, and real –world problems.
Peer learning and assessment: Collaborative activities for peer learning, teamwork, and mutual feedback.
Lab Work: session with hands-on exercises to apply data science concepts in R and SQL.
Problem-Based Learning: Engage students with practical, data-focused, and real –world problems.
Peer learning and assessment: Collaborative activities for peer learning, teamwork, and mutual feedback.
Grading System
Letter Grade (A-F)
Passing grade
D
Evaluation Plan
|
Type |
Percentage |
Assessment activity |
|
Assignments |
20-30 |
|
|
Midterm Exam |
10-15 |
Midterm 1 |
|
Midterm Exam |
10-15 |
Midterm 2 |
|
Final Exam |
15-20 |
|
|
Project |
25 |
Group project includes presentation (10%), report (10%), peer feedback (5%) |
|
Participation |
0-10 |
Class activities |
Course topics
- Overview of Operations Research in Decision Making Mathematical Optimization and Decision-Modeling Linear Programming and Graphical method Excel for Optimization and Solver Reports The Simplex Method Sensitivity Analysis and Real-World Applications Network Models and Applications Modelling problems using discrete-event simulations Queuing Theory and Practical Applications Case Studies in Operations Research Ethical Considerations and Trends in Operations Research
Notes:
- Course contents and descriptions, offerings and schedules are subject to change without notice.
- Students are required to follow all College policies including ones that govern their educational experience at VCC. Policies are available on the VCC website at:
https://www.vcc.ca/about/governance--policies/policies/. - To find out if there are existing transfer agreements for this course, visit the BC Transfer Guide at https://www.bctransferguide.ca.