Public View

You are viewing the public version of the syllabus. If you have a SUNet account, you can view the richer version of the syllabus after logging in.

IE 313
Operations Research III

Faculty Faculty of Engineering and Natural Sciences
Semester Spring 2025-2026
Course IE 313 - Operations Research III
Time/Place
Time
Week Day
Place
Date
10:40-11:30
Tue
FENS-L045
Feb 16-May 22, 2026
12:40-14:30
Thu
FENS-L045
Feb 16-May 22, 2026
Level of course Undergraduate
Course Credits SU Credit:3, ECTS:6, Basic:2, Engineering:4
Prerequisites IE 305
Corequisites IE 313R
Course Type Lecture

Instructor(s) Information

Sinan Yıldırım

Course Information

Catalog Course Description
Introduction to stochastic processes with examples based on the appropriate manufacturing and service systems; decision making under uncertainty; Markov chains; production/inventory models; queuing systems.
Course Learning Outcomes:
1. Have a basic knowledge of discrete time Markov chains (DTMCs), formulate suitable applications as DTMCs and analyze their transient and steady-state behaviors.
2. Have a basic knowledge of continuous time Markov chains (CTMCs), formulate suitable applications as CTMCs and analyze their transient and steady-state behaviors.
3. Develop a deeper understanding of certain CTMC classes including Poisson processes, birth-and-death processes, and queueing models.
4. Perform computation analysis of the stochastic processes of interest using a programming language.
Course Objective
The mission of this course is to continue studying the modeling and solution of decision problems using operations research techniques, with a particular emphasis on stochastic aspects.
Sustainable Development Goals (SDGs) Related to This Course:
Responsible Consumption and Production

Course Materials

Resources:
The following material will be shared on SUCourse.
- Textbook: Introduction to Stochastic Processes with R., Robert P. Dobrow, 1st Ed., Wiley. (Available at IC) https://risc01.sabanciuniv.edu/record=b2733539
The textbook is the main resource for the course.
- Recitations
- Python examples
- Everything covered in the lectures (take your own notes).
Technology Requirements:
Course-relevant Python skills are required and part of the evaluation.

Policies