Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Institute of Graduate Programmes
  • Department of Industrial Engineering
  • Doctorate Degree (Ph.D)
  • Course Structure Diagram with Credits
  • Dynamic Programming
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information
Code - Course Title ENM627 - Dynamic Programming
Course Type Elective Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) PROFESÖR DOKTOR ONUR KAYA
Mode of Delivery Face-to-face
Prerequisites No prerequisites or co-requisites
Courses Recomended Probability, Stochastic Models and Optimization courses are suggested to be taken before taking this course
Required or Recommended Resources Bertsekas, D. P., Dynamic Programming and Optimal Control, 3rd ed., Athena Scientific, Vols 1 and 2.
Recommended Reading List Bertsekas, D. P., Dynamic Programming and Optimal Control, 3rd ed., Athena Scientific, Vols 1 and 2.
Assessment methods and criteria 1 In-class midterm exam, 1 Final Exam, 1Project about writing a short paper and bi-weekly homeworks
Work Placement No internship or work practice required
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Dynamic Programming Algorithm: Introduction to Sequential Decision Making
Week - 2 Problem Formulation, Deterministic and Stochastic Dynamic Programming
Week - 3 Deterministic Dynamic Programming: Forward and Backward Recursion Algorithms, Shortest Path Problems
Week - 4 Markov Decision Processes
Week - 5 Problems with Perfect State Information
Week - 6 Structural Analysis for the Optimal Solutions of Dynamic Programming
Week - 7 Dynamic Programming Applications: Inventory Control, Dynamic Portfolio Analysis, Optimal Stopping Problems
Week - 8 Problems with Imperfect State Information
Week - 9 Reduction to Perfect State Info, Sufficient Statistics, Bayesian Models
Week - 10 Infinite Horizon Problems Stochastic Shortest Path, Bellman's Equation
Week - 11 Discounted Cost and Average Cost Analysis, Value and Policy Iteration Methods
Week - 12 Continuous Time Problems
Week - 13 Uniformization, Queueing Examples
Week - 14 Presentations of term projects

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Team/Group Work
  • Drill - Practise
  • Case Study
  • Problem Solving
  • Report Preparation and/or Presentation
  • Proje Design/Management

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 25
Homework 1 15
Project 1 20
Final Exam 1 40
Toplam (%) 100
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