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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS IND 4923 DISCRETE OPTIMIZATION MODELS AND ALGORITHMS ELECTIVE 3 0 0 5

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 goal of this course is to help students develop the ability to create and solve discrete and integer optimization models using exact and heuristic methods.

Learning Outcomes of the Course Unit

1   Knowing the application areas of discrete optimization, being able to define and formulate different problems 2   Ability to use exact solution methods for discrete optimization problems 3   Ability to use heuristic solution methods for discrete optimization problems 4   Ability to apply the methods explained in the course with the help of computer software 5   Ability to work as a team on a project using the methods explained in the course

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

END 3525 - OPERATIONS RESEARCH I

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Integer and combinatorial optimization models - 1 2 Integer and combinatorial optimization models - 2 3 Relaxations of optimization problems - 1 4 Relaxations of optimization problems - 2 5 Exact solution methods - 1 (Exhaustive search, branch-and-bound, branch-and-cut, branch-and-price, column generation methods) 6 Exact solution methods - 2 7 Exact solution methods - 3 8 Exact solution methods - 4 9 Heuristic/metaheuristic solution methods - 1 (greedy/constructive heuristics, local search, tabu search, simulated annealing, genetic algorithm) 10 Heuristic/metaheuristic solution methods - 2 11 Heuristic/metaheuristic solution methods - 3 12 Heuristic/metaheuristic solution methods - 4 13 Heuristic/metaheuristic solution methods - 5 14 Heuristic/metaheuristic solution methods - 6

Recomended or Required Reading

1. Lecture notes 2. Optimization in Operations Research (2nd ed.), Ronald L. Rardin, Prentice-Hall, USA, 2016 3. Introduction to Operations Research (10th ed.), F. S. Hillier, G. J. Lieberman, McGraw-Hill Inc., USA, 2015

Planned Learning Activities and Teaching Methods

Course Lectures / Problem Solving / Question-Answer

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 QUZ QUIZ 3 ASG ASSIGNMENT 4 FIN FINAL EXAM 5 FCG FINAL COURSE GRADE MTE * 0.20 + QUZ * 0.15 + ASG * 0.25 + FIN * 0.40 6 RST RESIT 7 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + QUZ * 0.15 + ASG * 0.25 + RST * 0.40

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)

Prof.Dr. Şeyda A. YILDIZ e-mail: seyda.topaloglu@deu.edu.tr Tel: (0232) 301 76 00

Office Hours

Will be announced later.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 2 28 Preparations before/after weekly lectures 14 1 14 Preparation for midterm exam 1 15 15 Preparation for final exam 1 20 20 Preparation for quiz etc. 2 2 4 Project Preparation 1 30 30 Midterm 1 2 2 Final 1 2 2 TOTAL WORKLOAD (hours) 115

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 LO.1 2 2 LO.2 2 2 LO.3 2 2 LO.4 2 2 LO.5 5 4 2 3