COURSE UNIT TITLE

: MATHEMATICAL MODELING AND LINEAR OPTIMIZATION

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5093 MATHEMATICAL MODELING AND LINEAR OPTIMIZATION ELECTIVE 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Statistics (English)
STATISTICS (ENGLISH)
Statistics (English)

Course Objective

The objective of this course is to cover an advance level of Linear Programming and to make the students to think about how organizations can perform linear programming in an effective way.

Learning Outcomes of the Course Unit

1   Defining basic concepts of linear programming
2   Expressing the industrial problems as linear decision models
3   Identifying transportation and assignment problems
4   Applying solution methods of linear decision problems, transportation and assignment problems
5   Interpreting the results economically
6   Suggesting alternatives to optimize the concerned objective
7   Planning for using the sources optimum

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Overview of operations research
2 Introduction to linear programming, construction of the LP model
3 Graphical LP solution, special cases
4 Standart LP form, preparing individual assignments
5 The simplex algorithm, artificial starting solution
6 Special cases in simplex method application
7 Dual problem and relationship between primal and dual solution
8 Mid-term exam
9 Economic interpretation of duality, dual simplex method
10 Sensitivity analysis, preparing individual assignments
11 The transportation algorithm
12 The Hungarian method, preparing presentations
13 Revised simplex algorithm, preparing presentations
14 Applications of linear programming models

Recomended or Required Reading

Textbook(s):
W.L. Winston, Operations Research, Applications and Algorithms, Duxbury Press, 1994.
H.A.Taha, Operations Research, Prentice Hall Int.Inc, 1997.
F.S. Hillier and G.J. Lieberman, Introduction to Operations Research, McGraw Hill Inc, 1990.
H.M. Wagner, Principles of Operations Research, Prentice Hall.
Supplementary Book(s):

Planned Learning Activities and Teaching Methods

Lecture and problem solving.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 PRS PRESENTATION
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE MTE* 0.30 + ASG * 0.20 + PRS * 0.10 + FIN * 0.40
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MTE* 0.30 + ASG * 0.20 + PRS * 0.10 + RST * 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams, homeworks and presentations.

Language of Instruction

English

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy.

Contact Details for the Lecturer(s)

DEU Fen Fakültesi Istatistik Bölümü
e-mail: cengiz.celikoglu@deu.edu.tr
Tel: 0232 301 85 50

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparation for midterm exam 1 10 10
Preparation for final exam 1 15 15
Preparing assignments 2 20 40
Preparing presentations 2 10 20
Preparations before/after weekly lectures 14 4 56
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 187

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1555
LO.2555
LO.3555
LO.4555
LO.5555
LO.6555
LO.7555