General
| School
|
School of Science
|
| Academic Unit
|
Department of Mathematics
|
| Level of Studies
|
Graduate
|
| Course Code
|
ΣΣΕ3
|
| Semester
|
1
|
| Course Title
|
Mathematical Programming
|
| Independent Teaching Activities
|
Lectures (Weekly Teaching Hours: 3, Credits: 7.5)
|
| Course Type
|
Special Background
|
| Prerequisite Courses
|
-
|
| Language of Instruction and Examinations
|
Greek
|
| Is the Course Offered to Erasmus Students
|
Yes (in English)
|
| Course Website (URL)
|
See eCourse, the Learning Management System maintained by the University of Ioannina.
|
Learning Outcomes
| Learning outcomes
|
The course learning outcomes are: the presentation of mathematical programming problems, the presentation of their solution techniques and their applications in several areas such as production, distribution, routing, etc. Upon successful completion of the course the student will be able to:
- model complex systems
- comprehend the mathematical foundation of the Simplex method and the dual theory
- select the appropriate algorithm for a particular optimization problem
- understand and apply the appropriate techniques required to solve linear optimization problems
- understand the principles of dynamic programming and apply dynamic programming solution techniques
- recognize and apply the appropriate inventory management policies (depending, each time, on underlying assumptions of the system)
|
| General Competences
|
- Working independently
- Decision-making
- Adapting to new situations
- Production of free, creative and inductive thinking
- Synthesis of data and information, with the use of the necessary technology
- Project planning and management
|
Syllabus
Linear programming problems formulation. The Simplex algorithm. Big M-method. Two-Phase method. Revised Simplex method. Duality theory. Dual Simplex algorithm. Sensitivity analysis. Parametric analysis. Transportation problem. Transhipment problem. Assignment problem. Dynamic programming: Bellman principle of optimality, finite and infinite horizon problems. Applications of dynamic programming. Inventory control.
Teaching and Learning Methods - Evaluation
| Delivery
|
Face-to-face
|
| Use of Information and Communications Technology
|
Lindo/Lingo Software, Mathematica, Email, Class Web
|
| Teaching Methods
|
| Activity
|
Semester Workload
|
| Lectures
|
39
|
| Study and analysis of bibliography
|
78
|
| Preparation of assignments and interactive teaching
|
70.5
|
| Course total
|
187.5
|
|
| Student Performance Evaluation
|
LANGUAGE OF EVALUATION: Greek
METHODS OF EVALUATION: Written work (30%), Final exam (70%).
|
Attached Bibliography
- Καρακώστας, Κ. (2002). Γραμμικά Μοντέλα: Παλινδρόμηση και Ανάλυση Διακύμανσης. Πανεπιστήμιο Ιωαννίνων.
- Λουκάς, Σ. (2014). Γενικό Γραμμικό Μοντέλο. Πανεπιστήμιο Ιωαννίνων.
- Οικονόμου, Π. και Καρώνη, Χ. (2010). Στατιστικά Μοντέλα Παλινδρόμησης, Εκδόσεις Συμεών.
- Draper, N.R. and H. Smith, (1998). Applied Regression Analysis, Third Edition, Wiley,
- Searle, S.R., (1997). Linear Models, Wiley Classics Library, Wiley,
- Seber, G.A.F. and A.J. Lee, (2003). Linear Regression Analysis, 2nd Edition, Wiley.