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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS ERA 3403 INTRODUCTION TO OPERATIONS RESEARCH ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR IPEK DEVECI KOCAKOÇ

Offered to

Econometrics (Evening) Econometrics

Course Objective

The main objective of the course is to give the student information and skills about defining, modelling, classifying, solving, interpreting of decision making problems in the management process.

Learning Outcomes of the Course Unit

1   To be able to classify decision making problems in the management process. 2   To be able to implement optimization modelling of problems on production and service systems. 3   To be able to expose solution alternatives of decision making problems. 4   To be able to apply operations research system methodology in solving inter-disiplinary problems 5   To be able to apply operations research system methodology in solving inter-disiplinary problems 6   To be able to interpret decision making problem solutions within economic analysis approach.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

HAZIRLIK - FOREIGN LANGUAGE PREPARATION CLASS

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to Operations Research [Historical Background, Definitions, Aims, Scientific Structure, Features (System Approach, Inter-disiplinary Approach, Scientific Methods) Operations Research Approach, Operations Research Methods, Selection of Operations Research Methods, Operations Research Application Areas, 2 Introduction to Optimization [Introduction, Optimization in Operations Research, Historical Background of Optimization, Definition of Optimization Problem, Classification of Optimization Problems, Optimization Techniques, Selection of Optimization Methods, Fundamentals of Optimization] 3 Classical Optimization Methods [Introduction, One-Variable Optimization, Unconstrained Multi-Variable Optimization, Equality Constrained Multi-Variable Optimization, Inequality Constrained Multi-Variable Optimization, Optimization in Economic Application, Evaluation of Classical Optimization Methods] 4 Introduction to Linear Programming 5 Graphical Method on Linear Programming, Algebraic Method on Linear Programming 6 Simplex Method on Linear Programming 7 Efficient Simplex Algorithms and Duality 8 Mid-term 9 Mid-term 10 Post Optimality Analysis 11 Particular Linear Programming Methods I: Transportation Models 12 Particular Linear Programming Methods II: Assignment Models 13 Particular Linear Programming Methods III:Traveler Salesman Model 14 Integer Programming

Recomended or Required Reading

1- TAHA, Hamdy A. : Operations Research : An Introduction , Sixth Ed., Macmillan Publishing Co., New York, 1997. 2- RAO, S. S. : Engineering Optimization; Theory and Practice ,Third Edition, Wiley Eastern Limited, New Delhi, 1996.

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 MTEG MIDTERM GRADE MTEG * 1 3 FIN FINAL EXAM 4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60 *** 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)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 12 3 36 Preparations before/after weekly lectures 12 2 24 Preparation for midterm exam 1 20 20 Preparation for final exam 1 32 32 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 114

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 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1 LO.6 1