Course Description
Formulation, analysis & critique of methods of mathematical modeling.
For information regarding prerequisites for this course, please refer to the Academic Course Catalog.
Course Guide
View this course’s outcomes, policies, schedule, and more.*
*The information contained in our Course Guides is provided as a sample. Specific course curriculum and requirements for each course are provided by individual instructors each semester. Students should not use Course Guides to find and complete assignments, class prerequisites, or order books.
Rationale
As members of a society increasingly managed by quantitative techniques and data-based decision making, the student benefits by learning how some of these techniques can best be employed. The emphasis is on creating mathematical models and understanding contexts in which mathematical tools are valuable.
Course Assignment
Textbook readings and lecture presentations
No details available.
After reading the Course Syllabus and Student Expectations, the student will complete the related checklist found in the Course Overview.
The student will complete a homework assignment for each module in the WebAssign that is associated with the course textbook. Typically, assignments will cover 4 – 5 sections from the textbook, but this will vary depending on the length and difficulty of each section included in the assignment. The student will be required to earn a 40% on each assignment before moving on to the next homework assignment or test (CLO: A, B, C, D, E, F; FSLOs: TSQR 1, 2, 3).
Project Assignments (4)
Project: The Travelling Salesman Assignment
This project introduces what is perhaps the most important integer program/network problem and applies a useful but incomplete linear programming relaxation of it. The project introduces subtours and constraint generation as methods of improving the initial relaxation. (CLO: A, B, C; FSLOs: TSQR 1, 2).
Project: The Subtour Assignment
This project builds on the linear relaxation developed in the first assignment. In this extension, the student analyzes solutions for more complex subtours and adaptively generates constraints until he/she is able utilize this strategy to achieve solutions to a particular problem instance. (CLO: A, B, C; FSLOs: TSQR 1, 2).
Project: Local Search Assignment
This project uses the Quadratic Assignment Problem to introduce the tremendously important topics of a neighborhood of a solution and of local search, which together form a powerful heuristic for approaching difficult problems. (CLO: A, B, C; FSLOs: TSQR 1, 2)
Project: Metaheuristic Assignment
This project extends the previous project by demonstrating the limitations of local search. This motivates two popular heuristics: Simulated Annealing and Tabu Search. The student is introduced to the mechanics and the methodologies of these approaches. (CLO: A, B, C; FSLOs: TSQR 1, 2)
Each quiz will cover the Learn material for two modules: the material assigned during the test module and the material from the previous module. Tests are not cumulative. Each test will be open-book/open-notes, contain 8-11 multiple-choice and short answer questions (with numerical answers or mathematical expressions), allow for one attempt, and have a 3-hour time limit. These tests will be completed in the WebAssign that is associated with the course textbook (CLO: A, B, C, D, E, F; FSLOs: TSQR 1, 2, 3).
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