Optimization with Engineering Applications

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Course Number: ENGRG 7070
Course Name: Optimization with Engineering Applications (Online)
Course Description:    Students will be able to solve a variety of optimization problems using optimization software or the optimization routines available in spreadsheets (e.g. Excel or Quattro). Linear, non-linear, and discrete problems will be solved. Students will learn the theory of improving search methods, which are the basis for all optimization algorithms. An emphasis will be placed on the need for the modeler to examine the practicality of program results. Also, students will perform a Life Cycle Analysis, which is an optimization procedure that minimizes the impacts on the environment.
Prerequisites:    None
Level: Graduate
Credits: 3
Format: Online

Registration Instructions

NOTE: The information below is representative of the course and is subject to change.  The specific details of the course will be available in the Desire2Learn course instance for the course in which a student registers.

Additional Information

Learning Outcomes

Upon completion of this course, you should be able to

  • Define the vocabulary associated with optimization methods and applications.
  • Design and solve linear and nonlinear optimization models with a spreadsheet optimizer.
  • Explain the differences between the various types of optimization tools, techniques, and algorithms.
  • Assess whether optimization techniques used in engineering applications are used effectively.
  • Complete a life cycle assessment.

Unit Descriptions

Unit 1 Overview: Basics of Optimization
In this unit we will learn the basics of optimization including vocabulary and some basic linear and nonlinear optimization techniques. We will also learn to use the Solver in Microsoft Excel to model and solve these basic optimization problems. These concepts form a foundation for what we will learn in the remaining units of the course.

Unit 2 Overview: Optimization Algorithms, Tools, and Techniques
This unit begins with lessons explaining some basic optimization algorithms.  The unit continues by covering sensitivity analysis.  This allows us to view the effect on the objective function value by modifying one decision variable at a time.  The unit concludes by presenting a few optimization techniques used to solve large and complex optimization problems where an exact solution cannot be found using the methods we've covered so far. Throughout Unit 2, you will work on a project in which you will apply the optimization techniques that you learned in Unit 1.

Unit 3 Overview: Additional Optimization Modeling Techniques and Methods
This unit focuses on additional types of optimization models regularly used to model and solve engineering and business problems. Specifically these types of models include blending, transportation, assignment, and network models. This unit has a group project in which current engineering literature is reviewed for usage of optimization techniques being used.

Unit 4: Life Cycle Assessment Overview
In this unit, we will learn about the Life Cycle Assessment (LCA) method in which we compare two alternative materials or processes to determine which has a smaller impact on the environment. LCA is used by many small and large corporations to help them realize where their products are causing the most pollution. LCA allows companies to optimize their processes to minimize environmental impact.

Grading Information

Grading Criteria
Homework 15%
Discussion 15%
Project 1 10%
Project 2 15%
Project 3 20%
Final Exam 25%
Total  100%
The group projects account for 45 percent of your grade, homework for 15 percent, discussions for 15 percent, and the final exam for the remaining 25 percent.
For each assignment, you will receive a grade in the Grades section of Desire2Learn. Grades will correspond to a numerical value from 1-100%. In general, you must score greater than 90% for an A, 80% for a B, 70% for a C, and 60% for a D. A grade of F will be assigned if you fail to demonstrate any understanding of the principles of optimization.

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