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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS CSC 5009 PRACTICAL PROGRAMMING WITH MATHEMATICAL MODELLING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSISTANT PROFESSOR AYŞE ÖVGÜ KINAY

Offered to

Ph.D. in Computer Science (English) Computer Science

Course Objective

Throughout this course, students are expected to know and understand common and important optimization problems. Students will develop problem modeling and solving skills and making appropriate decisions from the point of view of optimization.

Learning Outcomes of the Course Unit

1   Be able to understand the characteristics of different types of decision-making environments 2   Be able to identify the appropriate decision making approaches and tools. 3   Be able to formulate a valid operations research model 4   Be able to solve the model using computer software, and interpret the results of the model. 5   Be able to make recommendations to improve system operations based on an operations research analysis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Mathematical Programming Models 2 Solving Mathematical Programming Models 3 Linear Programming Models 4 Special Types of Mathematical Programming Models and Solution Methods 5 Non-linear Models 6 Integer Programming Models 7 Combinatorial Programming Models 8 Review of topics 9 Real life problems and solutions Food Manufacture I-II 10 Real life problems and solutions Factory Planning I-II 11 Real life problems and solutions Manpower Planning, Refinery Optimization 12 Real life problems and solutions Mining, Farm Planning, Economic Planning 13 Real life problems and solutions Decentralization, Curve Fitting 14 Real life problems and solutions Logical Design, Market Sharing

Recomended or Required Reading

Williams, H. P., 2003, Model Building in Mathematical Programming,Wiley, 4th editon.

Planned Learning Activities and Teaching Methods

Lecture format, built around the textbook readings with numerous examples chosen to illustrate theoretical concepts.

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)

ovgu.tekin@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparations before/after weekly lectures 14 3 42 Preparation for midterm exam 1 30 30 Preparation for final exam 1 40 40 Preparing assignments 1 30 30 Final 1 2 2 Midterm 1 2 2 TOTAL WORKLOAD (hours) 188

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