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Home » Course Catalogue » MBA Electives » RSM2408H – Modelling and Optimization for Decision Making

RSM2408H – Modelling and Optimization for Decision Making

General Information


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Applicable Major(s):
(c) = Core, (r) = Recommended

  • Management Analytics (c)
  • Process and Supply Chain Management (c)
  • Real Estate (r)

Target Audience

The course is intended for students interested in learning how to support decision making using business analytics. Specifically, we will focus on formulating models to enhance analytic decision making in applications such as finance, marketing, and operations. We will learn how to formulate, solve, and analyze models based upon optimization, decision trees, and simulation. These models and language will be required by any effective member of the C-suite in the future. This course is targeted at all MBA students.


12 weekly sessions.

Course Mission

  1. To improve students ability in the area of business analytics decision making supported by fact-based, data-driven, quantitative analysis;
  2. To develop, integrate and reinforce students’ modelling skills in a variety of applications;
  3. To enhance and reinforce students’ ability to intelligently use information in the presence of uncertainty. 

Course Scope

In this course, we will learn how to structure, analyze, and solve business decision problems using business analytics tools. We will focus on problems involving decision-making and risk analysis. The emphasis of the course will be on systematic, critical and logical thinking, and problem solving and their implementation. We will start with the basic techniques of good modeling and organization, and proceed to introduce a variety of modeling techniques and approaches. All along, we will critically think on how to interpret the results of our analysis process in the context of data driven decision-making. These will be illustrated by building and analyzing problems in finance, marketing, and operations. While the underlying concepts, models, and methods of this course are analytical in nature, we will develop them on intuitive and easy to use spreadsheets, always focusing on the ideas and insights, rather than the underlying mathematical details.  We will discuss the application of these methods with other software. We will study four specific techniques: sensitivity analysis (what if analysis), optimization, decision trees, and simulation. The usage of these techniques in practice can improve the decision making process in many situations. In many practical situations these techniques are essential for effective last steps as part of business analytics- prescribing decisions based on available data and its analysis.

Evaluation and Grade Distribution

ComponentDue DateWeight
Class ParticipationOngoing10%
Individual Cases 3-4 cases, one every 2-3 weeks50%
Group Cases3-4 cases, one every 2-3 weeks40%
Students will be given a number of opportunities to present in class (from the reading list, individual, or group cases)

Required Resources

  • Practical Management Science, Sixth Edition by Winston and Albright, South-Western. 
  • Additional readings are listed in the course outline and links are given to them on the Quercus.
  • Course package including a number of cases and readings.
  • Software: MS Excel, Palisades Decision Suite (available from Help Desk), Solver Table (free download)

Last Updated: 2022-01-25 @ 1:55 pm