Operations Research II (IE323) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Operations Research II IE323 Area Elective 3 2 0 4 8
Pre-requisite Course(s)
IE222
Course Language English
Course Type Elective Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Drill and Practice, Problem Solving.
Course Coordinator
Course Lecturer(s)
  • Assoc. Prof. Dr. Uğur BAÇ
  • Research Assistant İrem BULANIK ÖZDEMİR
Course Assistants
Course Objectives Students should have the ability to model and solve real life problems using operations research techniques and be able to analyze results obtained with such models. Student should understand the different types of models, such as deterministic vs. stochastic. Students should be able to use software to solve such models.
Course Learning Outcomes The students who succeeded in this course;
  • Students will be able to demonstrate an understanding of the operations research modeling approaches and techniques.
  • Students will be able to identify and formulate Integer Programming and Network problems; select and implement appropriate solution techniques.
  • Students will be able to develop and solve Integer Programming models using appropriate software packages.
  • Students will be able to demonstrate an understanding of the Project Management concept and techniques.
Course Content Modeling with integer variables; network models: model formulation, minimal spanning tree, shortest path, maximal flow problems, critical path method and program evaluation review techniques; nonlinear programming.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 A review of basic OR I and Introduction to Integer Programming [1] pg. 1-413 [1] pg. 475-476
2 Introduction to Integer Programming [1] pg. 475-476
3 Formulating (Mixed) Integer Programming Problems [1] pg. 477-511
4 Formulating (Mixed) Integer Programming Problems [1] pg. 477-511
5 Solving Integer Programming Problems-Relationship to Linear Programming-Branch and Bound [1] pg. 512-544
6 Solving Integer Programming Problems-Relationship to Linear Programming-Branch and Bound [1] pg. 512-544
7 Midterm I
8 Solving Integer Programming Problems-Implicit Enumeration-Cutting Plane Algorithm [1] pg. 545-561
9 Solving Integer Programming Problems-Implicit Enumeration-Cutting Plane Algorithm History of the Network Models, Terminology and Notation [1] pg. 545-561 [1] pg. 413-414
10 Midterm II Minimum Spanning Tree Problems-Prim’s algorithm, Kruskal’s algorithm [1] pg. 456-458
11 Minimum Spanning Tree Problems-Prim’s algorithm, Kruskal’s algorithm [1] pg. 456-458
12 Shortest Path Problems-Dijkstra’s algorithm [1] pg. 414-418
13 Maximum Flow Problems Ford-Fulkerson Algorithm, Max-flow Min-cut theorem [1] pg. 419-430
14 Maximum Flow Problems Ford-Fulkerson Algorithm, Max-flow Min-cut theorem Project Management, CPM and PERT, Crashing Project, Minimum Cost Network Flow Problems, Network Simplex [1] pg. 419-430 [1] pg. 431-449
15 Project Management, CPM and PERT, Crashing Project, Minimum Cost Network Flow Problems, Network Simplex [1] pg. 431-449
16 Project Management, CPM and PERT, Crashing Project, Minimum Cost Network Flow Problems, Network Simplex Introduction to Nonlinear Programming [1] pg. 431-449 [1] pg. 610-650

Sources

Course Book 1. Winston, W.L. Operations Research: Applications and Algorithms, 4rd Edition, Duxbury Press, Belmont, California, USA.
Other Sources 2. Hillier, F.S. and Lieberman, G.J., Introduction to Operations Research, 6th Edition, McGraw-Hill, 1995.
3. Wolsey, L.A., Integer Programming, Wiley-Interscience, 1st Edition, 1998.
4. Taha, H. A., Operations Research: An Introduction, Prentice Hall, 1996.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics 5 20
Homework Assignments - -
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 40
Final Exam/Final Jury 1 40
Toplam 8 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Has the ability to apply scientific knowledge gained in the undergraduate education and to expand and extend knowledge in the same or in a different area
2 Can apply gained knowledge and problem solving abilities in inter-disciplinary research
3 Has the ability to work independently within research area, to state the problem, to develop solution techniques, to solve the problem, to evaluate the obtained results and to apply them when necessary
4 Takes responsibility individually and as a team member to improve systematic approaches to produce solutions in unexpected complicated situations related to the area of study
5 Can develop strategies, implement plans and principles on the area of study and can evaluate obtained results within the framework
6 Can develop and extend the knowledge in the area and to use them with scientific, social and ethical responsibility
7 Has the ability to follow recent developments within the area of research, to support research with scientific arguments and data, to communicate the information on the area of expertise in a systematically by means of written report and oral/visual presentation
8 To have an oral and written communication ability in at least one of the common foreign languages ("European Language Portfolio Global Scale", Level B2)
9 Has software and hardware knowledge in the area of expertise, and has proficient information and communication technology knowledge
10 Follows scientific, cultural, and ethical criteria in collecting, interpreting and announcing data in the research area and has the ability to teach.
11 Has professional ethical consciousness and responsibility which takes into account the universal and social dimensions in the process of data collection, interpretation, implementation and declaration of results in mathematics and its applications.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory 16 2 32
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 4 64
Presentation/Seminar Prepration
Project
Report
Homework Assignments
Quizzes/Studio Critics 10 1 10
Prepration of Midterm Exams/Midterm Jury 2 10 20
Prepration of Final Exams/Final Jury 1 26 26
Total Workload 200