IE 438

Course ID:

Course Code & Number

IE 438

Course Title

Discrete Optimization

Level

BS

Credit Hours/ ECTS Credits

(3+0+0) 3 TEDU Credits, 5 ECTS Credits

Year of Study:

Semester:

Spring

Type of Course:

Elective

Mode of Delivery:

Face-to-face

Language of Instruction:

English

Pre-requisite / Co-requisite::

Catalog Description

Modeling, relaxing and bounding techniques. Fundamental easy-to-solve problems. Branch-and-bound, cutting planes method, branch and price and column generation methods. Dynamic programming. Meta-heuristics such as tabu search, genetic algorithms and variable neighborhood search. Application examples.

Course Objectives

The fundamental goal of this course is to provide a framework to solve optimization problems with discrete or integer variables. The course aims to teach the modeling, relaxing and bounding techniques. The topics will also include cutting plane algorithms, heuristics and approximation.

Software Usage

PC, Laptop, Java ILOG Cplex, Gams

Course Learning Outcomes

Upon succesful completion of this course, a student will be able to

1. Model optimization problems with discrete or integer variables (c),

2. Use relaxing and bounding techniques in discrete models (c),

3. Apply heuristic methods and approximation algorithms to find good solutions to integer programming models (h),

4. Use the branch and bound algorithm and the cutting plane algorithm for solving integer programming problems (i).

Learning Activities and Teaching Methods:

Telling/Explaining Discussion/Debate Questioning Reading Problem Solving Inquiry Collaborating Case Study/Scenarion Analysis Hands-on Activities

Assessment Methods and Criteria:

Test / Exam Quiz Lab Assignment Case Studies / Homework Presentation (Oral/Poster)

Assessment Methods and Criteria Others:

Design Content

Recommended Reading

1. Wolsey, L.A. (1998), Integer Programming, Wiley. 2. Nemhauser, G. L., Wolsey, L. A. (1999), Integer and Combinatorial Optimization, Wiley-Interscience. 3. Hillier, F. S., Lieberman, G. J. (2001), Introduction to Operations Research, McGraw-Hill Series in Industrial Engineering and Management Science. 4. Taha, H. A. (2007) Operations Research: An Introduction, Prentice-Hall. 5. Rardin, R.L. (1998), Optimization in Operations Research, Prentice-Hall. 6. Winston, W.L. (2004) Operations Research: Applications and Algorithms, CENGAGE Learning.

Required Reading

Course materials, lecture notes

Grading

Learning Activities and Teaching Methods Others:

Course Coordinator:

Student Workload:

Workload	Hrs
Hands-on Work	30
Exams/Quizzes	40
Oral Presentation	5
Team Meetings	15

Course & Program Learning Outcome Matching: