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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::
Pre-requisites: MATH 230 OR MATH 240
Co-requisites: NONE
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: