Master Program ın Engıneerıng Management

Mathematical Programming(EM 512)

Course Code Course Name Semester Theory Practice Lab Credit ECTS
EM 512 Mathematical Programming 1 4 0 0 3 8
Prerequisites
Admission Requirements
Language of Instruction English
Course Type Compulsory
Course Level Masters Degree
Course Instructor(s) E. Ertuğrul KARSAK ekarsak@gsu.edu.tr (Email)
Assistant
Objective The objective of the course is to familiarize the student with mathematical modeling and the basic tools of mathematical programming to the extent they can be applied in operational situations. The material is quite practical and will prove useful in personal decision making as well as in many types of engineering and management decisions.
Content - Introduction and organization
- Overview of modeling
- Introduction to linear programming
- Graphical solution
- Linear programming model
- Assumptions of linear programming
- Additional modeling examples
- The simplex method
- Standard form of linear programming
- Unrestricted variables
- Simplex method in tabular form
- Artificial starting solution
- The big M method
- The two-phase method
- Special cases in simplex method application
- Degeneracy
- Unbounded solutions
- Multiple optimal solutions
- Infeasible solution
- Post-optimality analysis
- Presentation of LINDO software
- Duality
- Definition of the dual problem
- Primal-dual relationships
- Dual simplex method
- Sensitivity analysis
- The transportation problem
- The assignment problem
- Introduction to network models
- Terminology of networks
- The minimum spanning tree problem
- Goal programming
- Deterministic dynamic programming
- Characteristics of dynamic programming problems
- Principle of optimality
- Selected dynamic programming applications
Course Learning Outcomes LO 1: Mathematical modeling
LO 2: Linear programming and simplex method
LO 3: Transportation and assignment models
LO 4: Network models
LO 5: Goal programming
LO 6: Deterministic dynamic programming
Teaching and Learning Methods Lecture; problem sessions; discussion; self study.
References - Hillier, F.S., Lieberman, G.J., Introduction to Mathematical Programming, McGraw-Hill, 1995.
- Taha, H.A., Operations Research: An Introduction, Tenth edition, Pearson, 2017.
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Theory Topics
Week Weekly Contents
Practice Topics
Week Weekly Contents
Contribution to Overall Grade
  Number Contribution
Contribution of in-term studies to overall grade 0 0
Contribution of final exam to overall grade 0 0
Toplam 0 0
In-Term Studies
  Number Contribution
Assignments 0 0
Presentation 0 0
Midterm Examinations (including preparation) 0 0
Project 0 0
Laboratory 0 0
Other Applications 0 0
Quiz 0 0
Term Paper/ Project 0 0
Portfolio Study 0 0
Reports 0 0
Learning Diary 0 0
Thesis/ Project 0 0
Seminar 0 0
Other 0 0
Toplam 0 0
No Program Learning Outcomes Contribution
1 2 3 4 5
Activities Number Period Total Workload
Total Workload 0
Total Workload / 25 0,00
Credits ECTS 0
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