• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Do sensitivity analysis.

   Contribution to Program Outcomes

   1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
   2. Develop solutions that yield optimal outputs by using limited resources efficiently
   3. Propose alternative point of views by analyzing complex industrial problems methodically
   4. Become an authority in modeling, simulation, process management and related subjects.
   5. Acquire scientific knowledge
   6. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Written exam
   2. Homework assignment
2. Apply decomposition techniques on large scale optimization problems.

   Contribution to Program Outcomes

   1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
   2. Develop solutions that yield optimal outputs by using limited resources efficiently
   3. Become an authority in modeling, simulation, process management and related subjects.
   4. Acquire scientific knowledge

   Method of assessment

   1. Written exam
   2. Homework assignment
3. Form mathematical models on a modeling language and obtain solutions by a mathematical program solver.

   Contribution to Program Outcomes

   1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
   2. Develop solutions that yield optimal outputs by using limited resources efficiently
   3. Propose alternative point of views by analyzing complex industrial problems methodically
   4. Become an authority in modeling, simulation, process management and related subjects.
   5. Acquire scientific knowledge
   6. Continuously develop their knowledge and skills in order to adapt to a rapidly developing technological environment

   Method of assessment

   1. Written exam
   2. Homework assignment
4. Apply problem dependent efficient algorithms of linear programing.

   Contribution to Program Outcomes

   1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
   2. Develop solutions that yield optimal outputs by using limited resources efficiently
   3. Propose alternative point of views by analyzing complex industrial problems methodically
   4. Acquire scientific knowledge
   5. Design and conduct research projects independently
   6. Continuously develop their knowledge and skills in order to adapt to a rapidly developing technological environment
   7. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Written exam
   2. Homework assignment

Contents
Week 1:	Linear and integer programming formulations, linearization
Week 2:	Simplex method and geometry in linear programming
Week 3:	Starting with Big-M and two phase methods, degeneracy and anti-cycling methods. Homework assignment 1
Week 4:	Duality
Week 5:	Dual simplex method
Week 6:	Sensitivity analysis. Homework assignment 2
Week 7:	Midterm exam
Week 8:	Bounded simplex method
Week 9:	Revised simplex method
Week 10:	Decomposition methods
Week 11:	Applications of decomposition methods. Homework assignment 3
Week 12:	Interior point method
Week 13:	Examples of interior point method
Week 14:	Integer programming and branch and bound. Homework assignment 4
Week 15*:	-
Week 16*:	Final exam
Textbooks and materials:	Linear Programming and Network Flows, M. Bazaraa, J. Jarvis, H. Sherali, 1990/4th, Wiley
Recommended readings:	Introduction to Linear Optimization, Bertsimas and Tsitsiklis, 1997, Athena Scientific
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	7	30
Other in-term studies:		0
Project:		0
Homework:	3, 6, 11, 14	30
Quiz:		0
Final exam:	16	40
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	13
Own studies outside class:	5	14
Practice, Recitation:	0	0
Homework:	7	4
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	10	2
Mid-term:	3	1
Personal studies for final exam:	12	2
Final exam:	4	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)