Optimization for Decision Making

Optimization for Decision Making

This course is part of Analytics for Decision Making Specialization

Instructor: Soumya Sen

Sponsored by Louisiana Workforce Commission

6,403 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

4.8

(68 reviews)

Beginner level

Recommended experience

13 hours to complete

3 weeks at 4 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

4.8

(68 reviews)

Beginner level

Recommended experience

13 hours to complete

3 weeks at 4 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments

Taught in English

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This course is part of the Analytics for Decision Making Specialization

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There are 4 modules in this course

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.

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.

What's included

11 videos1 reading2 assignments1 discussion prompt

11 videosTotal 91 minutes

"Analytics for Decision Making" Specialization Overview8 minutesPreview module
"Optimization for Decision Making" Course Overview11 minutes
Meet Your Instructor: Soumya Sen8 minutes
Module 1 Overview: An Introduction to Linear Programming1 minute
Modeling Approaches to Decision Making9 minutes
Applications of Optimization – Part 110 minutes
Applications of Optimization – Part 27 minutes
How to Setup a Linear Optimization Problem12 minutes
Oak Barrel Brewery: An Example Linear Program Problem10 minutes
Luxury Sofas Problem: A Practice Exercise3 minutes
Solution to the Luxury Sofas Practice Problem6 minutes

1 readingTotal 10 minutes

Read this article on Optimization in Prescriptive Analytics10 minutes

2 assignmentsTotal 120 minutes

Module 1 Practice Exercise60 minutes
Module 1 Graded Exercise60 minutes

1 discussion promptTotal 30 minutes

What are some problems you could reformulate into optimization problems?30 minutes

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.

What's included

8 videos2 assignments

8 videosTotal 61 minutes

Module 2 Overview2 minutesPreview module
Solving Linear Optimization Problems - Part 1a10 minutes
Solving Linear Optimization Problems - Part 1b7 minutes
Solving Linear Optimization Problems - Part 29 minutes
Solving Linear Optimization Problems - Part 34 minutes
Example Problem: How Many Items of Two Products Should a Manufacturer Produce? (Part 1)7 minutes
Example Problem: How to Plot the Feasible Region of the Problem (Part 2)8 minutes
Example Problem: Finding the Optimal Solution for the Problem (Part 3)10 minutes

2 assignmentsTotal 120 minutes

Module 2 Practice Exercise: A Retail Butcher Shop60 minutes
Module 2 Graded Exercise: Giapetto's Woodcarving Company60 minutes

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.

What's included

7 videos2 assignments

7 videosTotal 48 minutes

Module 3 Overview: Alternative Specifications and Shadow Prices0 minutesPreview module
Impact of Changes to Model Parameters - Part 111 minutes
Impact of Changes to Model Parameters - Part 210 minutes
What to Do When There are Multiple Optimal Solutions8 minutes
Redundant Constraints5 minutes
Unbounded Solutions7 minutes
Infeasible Solution4 minutes

2 assignmentsTotal 120 minutes

Module 3 Practice Exercise: An Automobile Manufacturer60 minutes
Module 3 Graded Exercise: Dorian Auto Manufacturer60 minutes

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.

What's included

13 videos2 readings2 assignments

13 videosTotal 74 minutes

Module 4 Overview: Modeling & Solving Linear Problems in Excel1 minutePreview module
Introduction to Spreadsheet Solvers4 minutes
Steps in Implementing an LP Model in Excel6 minutes
Organizing LP model data in Excel8 minutes
Optimal Solution with Excel Solver - Example 18 minutes
Optimal Solution with Excel Solver - Example 27 minutes
Optimal Solution with Excel Solver - Example 2 Solution4 minutes
Case Exercise: "Make vs. Buy" Decision (A Computer Gaming Company)4 minutes
Make vs. Buy Decision: LP Formulation9 minutes
Make vs. Buy Decision: Excel Solution Part 1 (Set Up the Problem)11 minutes
Make vs. Buy Decision: Excel Solution Part 2 (Using Excel Solver)4 minutes
Congratulations on Finishing the "Optimization for Decision Making" Course2 minutes
Carlson School of Management: Master of Science Program in Business Analytics (MSBA)2 minutes

2 readingsTotal 20 minutes

Carlson School of Management: MSBA Program Website10 minutes
Management Information Systems (MIS) Research Center10 minutes

2 assignmentsTotal 120 minutes

Module 4 Practice Exercise: Calmetal, a Manufacturing Firm Example60 minutes
Module 4 Graded Exercise: A Medical Device Example60 minutes

Instructor

Instructor ratings

5.0 (24 ratings)

Soumya Sen

University of Minnesota

5 Courses39,998 learners

Offered by

University of Minnesota

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4.8

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Reviewed on Sep 17, 2021

Very insightful course. Love the detail explaination for solving simple LP problems.

Reviewed on May 31, 2021

There are a lot of examples to work through and learn from which I find helps make the material easier to learn.

Reviewed on Apr 16, 2021

It was an interesting refreshed for the most part and went very quickly. Could have used just a little more info on using Excel Solver. Thanks for the class!

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