Linear Programming

Μάθημα: Linear Programming
Course Number: ECO1008
Ώρα:
Προαπαιτούμενα:
Μονάδες:
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Ημέρα:
Component(s): 5
Χάρτης: Map

COURSE OUTLINE

  1. GENERAL
SCHOOL AGRICULTURAL AND FORESTRY SCIENCES
DEPARTMENT AGRICULTURAL DEVELOPMENT
LEVEL OF STUDIES 7
COURSE CODE ECO1008 SEMESTER 8th
COURSE TITLE LINEAR PROGRAMMING
TEACHING ACTIVITIES
If the ECTS Credits are distributed in distinct parts of the course e.g. lectures, labs etc. If the ECTS Credits are awarded to the whole course, then please indicate the teaching hours per week and the corresponding ECTS Credits.		 TEACHING HOURS PER WEEK ECTS CREDITS
Lectures and exercises  (3+2) 5
     
     
Please, add lines if necessary. Teaching methods and organization of the course are described in section 4.    
COURSE TYPE

Background, General Knowledge, Scientific Area, Skill Development

 Specialization
PREREQUISITES:

 

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TEACHING & EXAMINATION LANGUAGE: Greek
COURSE OFFERED TO ERASMUS STUDENTS: Yes (in English)
COURSE URL: https://eclass.duth.gr/courses/OPE01249/
  1. LEARNING OUTCOMES
Learning Outcomes
Please describe the learning outcomes of the course: Knowledge, skills and abilities acquired after the successful completion of the course.
Upon the completion of the course the students;

·        should become capable of implementing Simplex method on agro – economic problems
General Skills
Name the desirable general skills upon successful completion of the module
Search, analysis and synthesis of data and information,

ICT Use

Adaptation to new situations

Decision making

Autonomous work

Teamwork

Working in an international environment

Working in an interdisciplinary environment

Production of new research ideas

 Project design and management

Equity and Inclusion

Respect for the natural environment

Sustainability

Demonstration of social, professional and moral responsibility and sensitivity to gender issues

Critical thinking

Promoting free, creative and inductive reasoning
  • Knowledge in Mathematics
  • Analytical and synthetically thinking

 
  1. COURSE CONTENT
1.       What is Linear Programming? A Preview,

2.       Types of Linear Problems

3.       The Linear Programming Model (Analytical, Matrix form)

4.       Transformation to a typical linear Programming problem, Core concepts (Feasible Area, Feasible Solution)

5.       Graphical Solution of Linear Programming Problem,

6.       Simplex Algorithm,

7.       Solution with Simplex,

8.       Normal Form, (M Form)

9.       Vogel solution

10.   Hungarian Method solution

11.   Optimum solution

12.   Sensitivity Analysis

13.   Duality
  1. LEARNING & TEACHING METHODS – EVALUATION
TEACHING METHOD
Face to face, Distance learning, etc.		 Face to face, Distance Learning with Microsoft Teams Platform
USE OF INFORMATION & COMMUNICATIONS TECHNOLOGY (ICT)
Use of ICT in Teaching, in Laboratory Education, in Communication with students		 §  Power point

§  e-class
TEACHING ORGANIZATION

The ways and methods of teaching are described in detail.

Lectures, Seminars, Laboratory Exercise, Field Exercise, Bibliographic research & analysis, Tutoring, Internship (Placement), Clinical Exercise, Art Workshop, Interactive learning, Study visits, Study / creation, project, creation, project. Etc.

 

The supervised and unsupervised workload per activity is indicated here, so that total workload per semester complies to ECTS standards.
Activity Workload/semester
Activity Semester workload

 
Lectures 39
Exercises 26
Individual study 60
   
Course total

(25-hour workload per credit unit)

  

125
Student Evaluation

Description of the evaluation process

 

Assessment Language, Assessment Methods, Formative or Concluding, Multiple Choice Test, Short Answer Questions, Essay Development Questions, Problem Solving, Written Assignment, Essay / Report, Oral Exam, Presentation in audience, Laboratory Report, Clinical examination of a patient, Artistic interpretation, Other/Others

 

Please indicate all relevant information about the course assessment and how students are informed 

 

Written exams at the end of the semester both on theory and optional midterm test.

Two tests are taken within the semester and the average of the grade of those tests is multiplied by 0,3 and is added to the grade of the final test. The precondition for the validity of this bonus is the grade of the final test to be equal or over three. 2. Assignments are delegated to the students that are graded with ranking 0-2. The grade of this assignment is added to the final grade of the semester The precondition for the validity of this bonus is the grade of the final test to be equal or over three

 

 
  1. SUGGESTED BIBLIOGRAPHY
§           Koutroumanidis Th., Zafeiriou E., Malesios Ch., Applied Mathematics for Agriculture (in Greek)

§  M Loukakis Linear Programming

§  Paparrizos K. Linear Programming, Algorithms and Applications

 

 

 

 

 

ANNEX OF THE COURSE OUTLINE

 

Alternative ways of examining a course in emergency situations

 

Teacher (full name): Eleni Zafeiriou
Contact details: ezafeir@agro.duth.gr
Supervisors: (1) Yes
Evaluation methods: (2) Written Exam /Mid term test
Implementation Instructions: (3) The subjects of the exams will be provided through a file that will be uploaded in the field ‘projects’ for a specific time period according to the program of exams.

The answers by the students will be provided by the students through multimedia files.

Each exercise will be graded while the exam contribution will be 100%,

The exam is simultaneous for all the students

The participation in the exams can be done with the use of their institutional account.