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Optimization for Decision Making

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Optimization for Decision Making

Name: Optimization for Decision Making
Rating: 4.733333333333333 (90 reviews)

This course is part of Analytics for Decision Making Specialization

Instructor: Soumya Sen

9,780 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

90 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

90 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Microsoft Excel

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.

Module details

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 minutes
"Optimization for Decision Making" Course Overview11 minutes
Meet Your Instructor: Soumya Sen9 minutes
Module 1 Overview: An Introduction to Linear Programming1 minute
Modeling Approaches to Decision Making10 minutes
Applications of Optimization – Part 110 minutes
Applications of Optimization – Part 28 minutes
How to Setup a Linear Optimization Problem12 minutes
Oak Barrel Brewery: An Example Linear Program Problem10 minutes
Luxury Sofas Problem: A Practice Exercise4 minutes
Solution to the Luxury Sofas Practice Problem7 minutes

1 readingTotal 10 minutes

Read this article on Optimization in Prescriptive Analytics10 minutes

2 assignmentsTotal 120 minutes

Module 1 Graded Exercise60 minutes
Module 1 Practice 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 minutes
Solving Linear Optimization Problems - Part 1a10 minutes
Solving Linear Optimization Problems - Part 1b8 minutes
Solving Linear Optimization Problems - Part 210 minutes
Solving Linear Optimization Problems - Part 35 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)11 minutes

2 assignmentsTotal 120 minutes

Module 2 Graded Exercise: Giapetto's Woodcarving Company60 minutes
Module 2 Practice Exercise: A Retail Butcher Shop60 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 49 minutes

Module 3 Overview: Alternative Specifications and Shadow Prices1 minute
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 Solutions9 minutes
Redundant Constraints6 minutes
Unbounded Solutions8 minutes
Infeasible Solution4 minutes

2 assignmentsTotal 120 minutes

Module 3 Graded Exercise: Dorian Auto Manufacturer60 minutes
Module 3 Practice Exercise: An Automobile 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 75 minutes

Module 4 Overview: Modeling & Solving Linear Problems in Excel1 minute
Introduction to Spreadsheet Solvers4 minutes
Steps in Implementing an LP Model in Excel6 minutes
Organizing LP model data in Excel9 minutes
Optimal Solution with Excel Solver - Example 19 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)12 minutes
Make vs. Buy Decision: Excel Solution Part 2 (Using Excel Solver)4 minutes
Congratulations on Finishing the "Optimization for Decision Making" Course3 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 Graded Exercise: A Medical Device Example60 minutes
Module 4 Practice Exercise: Calmetal, a Manufacturing Firm Example60 minutes

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Instructor

Instructor ratings

(32 ratings)

Soumya Sen

University of Minnesota

11 Courses54,105 learners

Offered by

University of Minnesota

Learner reviews

5 stars
80%
4 stars
15.55%
3 stars
3.33%
2 stars
0%
1 star
1.11%

Showing 3 of 90

Reviewed on May 15, 2021

Good teaching style with step by step guidance. Thanks for the connecting high school math (that I learned many years ago) to real life context. I look forward to the next course.

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 May 19, 2023

I love the calmness and the fact the course was detailed and concise as possible. Professor Soumya Sen is a great teacher.

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