INDUSTRIAL ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
INE3009 Operations Research II Fall 3 2 4 6
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Must Course
Course Level: Bachelor
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi AYŞE KAVUŞTURUCU
Course Lecturer(s): RA ESRA ADIYEKE
Dr. Öğr. Üyesi AYŞE KAVUŞTURUCU
Course Objectives: The goal of this course is to develop and extend the students knowledge of mathematical techniques underlying the application of Operations Research. It aims to give students a good foundation in modeling and solving Linear, Nonlinear optimization problems, Network modeling, Dynamic programming problems and making business decisions in random (stochastic) environments.

Learning Outputs

The students who have succeeded in this course;
I. Be able to recognize the multiobjective nature of goal programming,
II. Understand the nature of transportation and assignment models as well as other network models, and be able to apply them.
III. Be able to develop dynamic programming strategies for various OR problems,
IV. Understand the probabilistic nature of various OR problems and be able to apply appropriate queueing models to such problems.
V. Understand the concept of Nonlinear Programming and Search Methods.

Course Content

This course on operations research covers Linear, Nonlinear unconstrained programming problems and their solution methods. Selected deterministic dynamic programming applications are also presented. In the second part of the course, some decision making procedures under uncertainty such as Markov Chains and queuing systems are analyzed.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Goal PRogramming
2) Goal Programming (con't)
3) Transportation Model
4) Transportation Model, Assignment Model/Network Modeling; Minimal Spanning Tree Algorithm, Shortest Route Problem
5) Network Modeling; Maximal Flow Model
6) Dynamic Programming; Recursive Nature of DP, Shortest Route Problem
7) Dynamic Programming; Resource Allocation Problem, Inventory Problem
8) Dynamic Programming; Resource Allocation Problem, Inventory Problem
9) Dynamic Programming; The Knapsack, Fly-Away Kit, Cargo Loading Model
10) Markov Chains; Transition Probabilities and Classification of States in Markov Chain
11) Queueing Models; General Structure, Birth and Death Process
12) Queueing Models; Single Server Models, Multiple Server Models
13) Nonlinear Programming; Unconstrained Algorithms; Direct Search Method, Gradient Method
14) Nonlinear Programming; Unconstrained Algorithms; Direct Search Method, Gradient Method

Sources

Course Notes: Taha, Hamdy A., Operations Research, 8th edition, 2007. ISBN: 0131360140
References: Winston, Wayne L., Operations Research: Applications and Algorithms, 4th edition, 2003. ISBN-13: 978-0534380588 Hillier, F.S. and Lieberman, G.J., Introduction to Operations Research, 8th edition, 2005. ISBN 007-123828-X

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes 4 % 25
Homework Assignments % 0
Presentation % 0
Project % 0
Seminar % 0
Midterms 1 % 35
Preliminary Jury % 0
Final 1 % 40
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 0 0 0
Application 14 4 56
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 14 2 28
Presentations / Seminar 0 0 0
Project 0 0 0
Homework Assignments 0 0 0
Quizzes 4 1 4
Preliminary Jury 0 0 0
Midterms 1 1 1
Paper Submission 0 0 0
Jury 0 0 0
Final 1 2 2
Total Workload 133

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Build up a body of knowledge in mathematics, science and industrial engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems. 5
2) Identify, formulate, and solve complex engineering problems; select and apply proper analysis and modeling methods for this purpose. 5
3) Design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. The ability to apply modern design methods to meet this objective. 5
4) Devise, select, and use modern techniques and tools needed for solving complex problems in industrial engineering practice; employ information technologies effectively. 4
5) Design and conduct experiments, collect data, analyze and interpret results for investigating the complex problems specific to industrial engineering.
6) Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working independently.
7) Demonstrate effective communication skills in both oral and written English and Turkish. Writing and understanding reports, preparing design and production reports, making effective presentations, giving and receiving clear and understandable instructions.
8) Recognize the need for lifelong learning; show ability to access information, to follow developments in science and technology, and to continuously educate him/herself.
9) Develop an awareness of professional and ethical responsibility, and behaving accordingly. Information about the standards used in engineering applications.
10) Know business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
11) Know contemporary issues and the global and societal effects of modern age engineering practices on health, environment, and safety; recognize the legal consequences of engineering solutions.
12) Develop effective and efficient managerial skills. 4