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University of Minnesota

Optimization for Decision Making

University of Minnesota via Coursera

Overview

In this data-driven world, companies are often interested in knowing what is the "best" course of action, given the data. For example, manufacturers need to decide how many units of a product to produce given the estimated demand and raw material availability? Should they make all the products in-house or buy some from a third-party to meet the demand? Prescriptive Analytics is the branch of analytics that can provide answers to these questions. It is used for prescribing data-based decisions. The most important method in the prescriptive analytics toolbox is optimization. This course will introduce students to the basic principles of linear optimization for decision-making. Using practical examples, this course teaches how to convert a problem scenario into a mathematical model that can be solved to get the best business outcome. We will learn to identify decision variables, objective function, and constraints of a problem, and use them to formulate and solve an optimization problem using Excel solver and spreadsheet.

Syllabus

  • Module 1: Introduction to Linear Programming
    • Prescriptive analytics is a part of business analytics that is aimed at prescribing solutions to decision problems. The most important modeling technique within prescriptive analytics is optimization. In this module, we will learn how to recognize contexts where it can be applied and get introduced to the basics of linear optimization.
  • Module 2: Solving Linear Programs
    • In order to solve linear optimization problems (i.e., linear programs), we can use graphical methods for basic example problems. For higher dimensional problems, we will use tools like Excel Solver later in the course. The benefit of using graphical methods is that it gives us an intuition into how these problems can be solved.
  • Module 3: Alternative Specifications & Special Cases in Linear Optimization
    • In this module we will explore what happens when the model parameters are changed. We will also look at special cases of linear optimization problems.
  • Module 4: Modeling & Solving Linear Problems in Excel
    • Having learned how to formulate linear optimization problem and the graphical methods for solving them, we are now going to start solving larger problems using Excel Solver. This module provides an overview of how to set up and solve these decision problems using Excel.

Taught by

Soumya Sen

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