• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS IND 4903 DISCRETE OPTIMIZATION MODELS AND ALGORITHMS ELECTIVE 3 0 0 4

Offered By

Industrial Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ŞEYDA AYŞE YILDIZ

Offered to

Industrial Engineering

Course Objective

The aim of this course is to present classic integer and combinatorial optimization models forms to students and to make them capable of solving these models using exact and heuristic solution approaches.

Learning Outcomes of the Course Unit

1   To be able know the application areas of discrete optimization and identify different problems 2   To be able to know exact solution methods for discrete optimization problems 3   To be able to use exact solution methods 4   To be able to know heuristic solution methods for discrete optimization problems 5   To be able to use heuristic solution methods

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Integer and Combinatorial Optimization Models 2 Integer and Combinatorial Optimization Models 3 Relaxations of Optimization Problems 4 Relaxations of Optimization Problems 5 Enumerative Algorithms (Total Enumeration, Branch-and-Bound, Branch-and-Cut, Branch-and-Price, Column Generation) 6 Enumerative Algorithms Continue 7 Enumerative Algorithms Continue 8 Heuristic Discrete Optimization (Greedy/Constructive Heuristics, Local Improvement, Tabu Search, Simulated Annealing, Genetic Algorithms) 9 Midterm Exam 10 Heuristic Discrete Optimization Continues 11 Heuristic Discrete Optimization Continues 12 Discrete Dynamic Programming 13 Homework Presentations 14 Homework Presentations Continue

Recomended or Required Reading

1- Optimization in Operations Research, Ronald L. Rardin, Prentice-Hall, USA, 1998 2- Introduction to Operations Research, F. S. Hillier, G. J. Lieberman, McGraw-Hill Inc., USA, 2005

Planned Learning Activities and Teaching Methods

Lecture / Question-Answer / Discussion / Problem Solving

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 ASG ASSIGNMENT 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + ASG * 0.20 + FIN * 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.20 + RST * 0.50 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Assoc.Prof. Dr. Şeyda Topaloğlu, seyda.topaloglu@deu.edu.tr

Office Hours

Assoc.Prof.Dr. Şeyda Topaloğlu, Afternoons on Monday and Tuesday

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 11 3 33 Preparations before/after weekly lectures 11 1 11 Preparation for midterm exam 1 15 15 Preparation for final exam 1 20 20 Preparation for homework 1 20 20 Midterm 1 1,5 2 Final 1 2 2 TOTAL WORKLOAD (hours) 103

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 PO.12 LO.1 4 3 5 5 3 LO.2 5 LO.3 5 LO.4 5 LO.5 5