IE 332

Course ID:

Course Code & Number

IE 332

Course Title

Mathematical Modeling & Optimization III

Level

BS

Credit Hours/ ECTS Credits

(3+0+0) 3 TEDU Credits, 6 ECTS Credits

Year of Study:

Junior

Semester:

Spring

Type of Course:

Compulsory

Mode of Delivery:

Face-to-face

Language of Instruction:

English

Pre-requisite / Co-requisite:

Catalog Description

Decision making under uncertainty. Dynamic programming. Introduction to stochastic processes. Queuing models and applications. Markov chains and Markov processes.

Course Objectives

The goal of this course is to teach decision making under uncertainty. The course introduces the nature and basic concepts of stochastic processes first and then teaches the following stochastic modeling techniques: Markov models, queuing models, dynamic programming, and Markov decision processes. The course also aims to familiarize students with the computer tools for solving decision making problems involving such models.

Software Usage

Course Learning Outcomes

Upon succesful completion of this course, a student will be able to
1. Define stochastic processes, (B1, a2)
2. Formulate an existing stochastic environment by using stochastic processes, Markov chains, or queuing models, (B3, e)
3. Address stochastic dynamic programming models, (B3, e)
4. Propose preferred solution alternatives based on sensitivity and scenario analysis after evaluating the performance of a process, component or system, (B6, e)
5. Apply programming knowledge to solve stochastic decision-making problems, (B3, k)

Learning Activities and Teaching Methods:

Telling/Explaining Discussion/Debate Questioning Problem Solving Inquiry Collaborating Hands-on Activities

Assessment Methods and Criteria:

Test / Exam Quiz

Assessment Methods and Criteria Others:

Design Content

Recommended Reading

Hillier F.S. and Lieberman G.J. (2010), Introduction to Operations Research (9th edition), McGraw-Hill

Required Reading

Winston W. L. (2004), Operations Research (4th edition), Duxbury

Grading

Learning Activities and Teaching Methods Others:

Course Coordinator:

Sırma Karakaya

Student Workload:

Workload	Hrs
Lectures	42
Course Readings	28
Observation	8
Exams/Quizzes	50
Homeworks	40

Course & Program Learning Outcome Matching: