Syllabus ( IE 202 )
|
|
Basic information
|
|
| Course title: |
Discrete Optimization |
| Course code: |
IE 202 |
| Lecturer: |
Assist. Prof. Figen ÖZTOPRAK TOPKAYA
|
| ECTS credits: |
6 |
| GTU credits: |
3 () |
| Year, Semester: |
2, Spring |
| Level of course: |
First Cycle (Undergraduate) |
| Type of course: |
Compulsory
|
| Language of instruction: |
English
|
| Mode of delivery: |
Face to face
|
| Pre- and co-requisites: |
None |
| Professional practice: |
No |
| Purpose of the course: |
The aim of the course is to introduce basic concepts of discrete optimization by developing both theory and practice |
|
|
|
Learning outcomes
|
|
Upon successful completion of this course, students will be able to:
-
Design discrete optimization models (linear and integer) for various problems.
Contribution to Program Outcomes
-
Understand, use and interpret scientific concepts related to engineering and mathematics fields
-
Use mathematical modelling, statistical applications, analysis methods and techniques and the system approach necessary in industrial engineering applications
-
Detect a problem to be solved, formulate it and develop a solution model
-
Adhere to life-long learning principle and learn how to learn
Method of assessment
-
Written exam
-
Homework assignment
-
Term paper
-
Identify, formulate and solve engineering problems by using discrete optimization methods.
Contribution to Program Outcomes
-
Understand, use and interpret scientific concepts related to engineering and mathematics fields
-
Use mathematical modelling, statistical applications, analysis methods and techniques and the system approach necessary in industrial engineering applications
-
Detect a problem to be solved, formulate it and develop a solution model
-
Adhere to life-long learning principle and learn how to learn
Method of assessment
-
Written exam
-
Homework assignment
-
Term paper
-
Solve the optimization problems via optimization software
Contribution to Program Outcomes
-
Understand, use and interpret scientific concepts related to engineering and mathematics fields
-
Use mathematical modelling, statistical applications, analysis methods and techniques and the system approach necessary in industrial engineering applications
-
Detect a problem to be solved, formulate it and develop a solution model
-
Take leadership role and use initiative
Method of assessment
-
Written exam
-
Homework assignment
-
Term paper
|
|
|
Contents
|
|
| Week 1: |
Review: Linear programming |
| Week 2: |
Introduction to discrete optimization: theoretical & practical comparison of continuous and discrete optimization |
| Week 3: |
Introduction to computational complexity: P vs NP |
| Week 4: |
Integer programming |
| Week 5: |
Branch and bound method - Homework 1 |
| Week 6: |
Introduction to logic, proof methods |
| Week 7: |
Mixed-integer, binary programming - Homework 2 |
| Week 8: |
Knapsack, bin packing, cutting stock problems |
| Week 9: |
Cutting plane methods |
| Week 10: |
Applications: modeling & solving integer programming problems via optimization software - Homework 3 |
| Week 11: |
Network models: Notation, min cost flow, min spanning tree |
| Week 12: |
Hamiltonian cycle, travelling salesman problem - Homework 4 |
| Week 13: |
Network simplex method |
| Week 14: |
Applications: modeling & solving network problems via optimization software - Homework 5 |
| Week 15*: |
-- |
| Week 16*: |
Final exam |
| Textbooks and materials: |
Introduction to Operations Research, Hillier and Lieberman McGraw-Hill, 7th Edition, 2002.
Logic and Integer Programming, Williams, Springer, 2009th edition, 2009.
|
| Recommended readings: |
-
|
|
|
* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
|
|
|
|
|
Assessment
|
|
|
| Method of assessment |
Week number |
Weight (%) |
|
| Mid-terms: |
9 |
35 |
| Other in-term studies: |
|
0 |
| Project: |
|
0 |
| Homework: |
5,7,10,12,14 |
30 |
| Quiz: |
|
0 |
| Final exam: |
16 |
35 |
| |
Total weight: |
(%) |
|
|
|
|
Workload
|
|
|
| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
|
| Courses (Face-to-face teaching): |
3 |
14 |
|
| Own studies outside class: |
2 |
14 |
|
| Practice, Recitation: |
0 |
0 |
|
| Homework: |
6 |
6 |
|
| Term project: |
10 |
1 |
|
| Term project presentation: |
0 |
0 |
|
| Quiz: |
0 |
0 |
|
| Own study for mid-term exam: |
13 |
1 |
|
| Mid-term: |
2 |
1 |
|
| Personal studies for final exam: |
15 |
1 |
|
| Final exam: |
4 |
1 |
|
| |
|
Total workload: |
|
| |
|
Total ECTS credits: |
* |
|
|
* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
|
|
|
-->
