Main Page     Information on the Institution     Degree Programs     General Information for Students     Türkçe  

 DEGREE PROGRAMS


 Associate's Degree (Short Cycle)


 Bachelor’s Degree (First Cycle)


 Master’s Degree (Second Cycle)

  Course Description
Course Name : Discrete Optimization

Course Code : EM-551

Course Type : Optional

Level of Course : Second Cycle

Year of Study : 1

Course Semester : Fall (16 Weeks)

ECTS : 6

Name of Lecturer(s) : Instructor MELİK KOYUNCU

Learning Outcomes of the Course : Gains the knowledge about linear models
Models a problem via linear programming
Solves the linear programming model by using appropriate technique
Solves and models the problems by using integer programming
Models the planning problems by goal programming
Has the knowledge about network models

Mode of Delivery : Face-to-Face

Prerequisites and Co-Prerequisites : None

Recommended Optional Programme Components : None

Aim(s) of Course : This course aims to provide the capability to solve the problems in manufacturing and service systems by using linear programming techniques.

Course Contents : Introduction to modelling, Simplex method, branch and bound algorithm, additive algortithm, transportation problems, Assignment problems, Introduction to netwok models, Introduction to netwok models, Minimum spanning tree problem and its applications, maximum flow problem and its applications, Case studies about linear programming

Language of Instruction : Turkish

Work Place : Class room


  Course Outline /Schedule (Weekly) Planned Learning Activities
Week Subject Student's Preliminary Work Learning Activities and Teaching Methods
1 Introduction to modelling reading the related chapter from text book Power point presentation and lecture
2 Simplex method reading the related chapter from text book Power point presentation and lecture
3 branch and bound algorithm reading the related chapter from text book Power point presentation and lecture
4 additive algortithm reading the related chapter from text book Power point presentation and lecture
5 transportation problems reading the related chapter from text book Power point presentation and lecture
6 Assignment problems reading the related chapter from text book Power point presentation and lecture
7 Midterm exam
8 Introduction to netwok models reading the related chapter from text book Power point presentation and lecture
9 Shortest path problems and its applications reading the related chapter from text book Power point presentation and lecture
10 Minimum spanning tree problem and its applications reading the related chapter from text book Power point presentation and lecture
11 maximum flow problem and its applications reading the related chapter from text book Power point presentation and lecture
12 Case studies about linear programming reading the related chapter from text book Power point presentation and lecture
13 Case studies about linear programming reading the related chapter from text book Power point presentation and lecture
14 presantation of projects reading the related chapter from text book Power point presentation and lecture
15 optimization softwares reading the related chapter from text book Power point presentation and lecture
16/17 Final exam Written exam


  Required Course Resources
Resource Type Resource Name
Recommended Course Material(s)  
 
Required Course Material(s)


  Assessment Methods and Assessment Criteria
Semester/Year Assessments Number Contribution Percentage
    Mid-term Exams (Written, Oral, etc.) 1 80
    Homeworks/Projects/Others 1 20
Total 100
Rate of Semester/Year Assessments to Success 40
 
Final Assessments 100
Rate of Final Assessments to Success 60
Total 100

  Contribution of the Course to Key Learning Outcomes
# Key Learning Outcome Contribution*
1 Understand, interpret and apply knowledge in his/her field domain both in-depth and in-breadth by doing scientific research in industrial engineering. 5
2 Acquire comprehensive knowledge about methods and tools of industrial engineering and their limitations. 5
3 Work in multi-disciplinary teams and take a leading role and responsibility. 5
4 Identify, gather and use necessary information and data. 5
5 Complete and apply the knowledge by using scarce and limited resources in a scientific way and integrate the knowledge into various disciplines. 4
6 Keep up with the recent changes and applications in the field of Industrial Engineering and analyze these innovations when necessary. 4
7 Work in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. 3
8 Analyze Industrial Engineering problems, develop innovative methods to solve the problems. 5
9 Have the ability to propose new and/or original ideas and methods in developing innovative solutions for designing systems, components or processes. 5
10 Design and perform analytical modeling and experimental research and analyze/solve complex matters emerged in this process. 5
11 Follow, study and learn new and developing applications of industrial engineering. 5
12 Use a foreign language in verbal and written communication at least B2 level of European Language Portfolio. 4
13 Present his/her research findings systematically and clearly in oral and written forms in national and international platforms. 4
14 Understand social and environmental implications of engineering practice. 4
15 Consider social, scientific and ethical values in the process of data collection, interpretation and announcement of the findings. 4
* Contribution levels are between 0 (not) and 5 (maximum).

  Student Workload - ECTS
Works Number Time (Hour) Total Workload (Hour)
Course Related Works
    Class Time (Exam weeks are excluded) 16 3 48
    Out of Class Study (Preliminary Work, Practice) 16 6 96
Assesment Related Works
    Homeworks, Projects, Others 1 10 10
    Mid-term Exams (Written, Oral, etc.) 1 2 2
    Final Exam 1 2 2
Total Workload: 158
Total Workload / 25 (h): 6.32
ECTS Credit: 6