Industrial Engineering

Operations Research I(IND371)

Content
Course Code Course Name Semester Theory Practice Lab Credit ECTS
IND371 Operations Research I 5 4 0 0 4 5
Prerequisites ING207
Admission Requirements ING207
Language of Instruction French
Course Type Compulsory
Course Level Bachelor Degree
Course Instructor(s) E. Ertuğrul KARSAK ekarsak@gsu.edu.tr (Email) Nazlı GÖKER MUTLU nagoker@gsu.edu.tr (Email) Nazlı GÖKER MUTLU nagoker@gsu.edu.tr (Email)
Assistant
Objective The aim of this course is to equip students with pertinent modeling and mathematical programming knowledge and skills for solving decision making problems.
Content - Introduction
- Stages of modeling
- Introduction to linear programming
- Graphical solution
- Linear programming model
- Assumptions of linear programming
- Additional examples of linear programming
- Simplex method
- Algebra of the simplex method
- Simplex method in tabular form
- Artificial variables technique
- Big M method
- Two-phase method
- Degeneracy; Alternative optimal solutions; Unbounded solution; Infeasible solution
- Post-optimality analysis
- Quiz I
- Theory of the simplex method
- Revised simplex method
- Duality
- Duality theory
- Economic interpretation of duality
- Complementary slackness theorem
- Midterm
- Presentation of an LP solver
- Dual simplex method
- Sensitivity analysis
- Bounded variables technique
- Transportation problem
- Definition of the transportation problem
- Finding an initial basic feasible solution
- Transportation simplex method
- Assignment problem
- Quiz 2
- Network models
- Terminology of networks
- Shortest-path problem
- Minimum spanning tree problem
- Dynamic programming
- Introduction
- Principle of optimality
- Examples of deterministic dynamic programming
Course Learning Outcomes At the end of this course, students will acquire the following skills:
1. Mathematical modeling,
2. Linear programming,
3. Transportation and assignment problems,
4. Network models,
5. 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.
- Bazaraa, M.S., Jarvis, J.J., Sherali, H.D., Linear Programming and Network Flows, John Wiley & Sons, 1990.
- Taha, H.A., Operations Research: An Introduction, Sixth edition, Prentice-Hall, 1997.
Print the course contents
Weekly Contents
Theory Topics
Week Weekly Contents
1 Stages of modeling; Introduction to linear programming; Graphical solution
2 Linear programming model; Assumptions of linear programming; Additional examples of linear programming
3 Simplex method; Algebra of the simplex method; Simplex method in tabular form
4 Artificial variables technique; Big M method; Two-phase method
5 Degeneracy, alternative optima, unbounded solution, infeasible solution; Post-optimality analysis
6 Theory of the simplex method; Revised simplex method
7 Duality; Duality theory; Economic interpretation of duality; Complementary slackness theorem
8 Midterm
9 Presentation of an LP solver; Dual simplex method
10 Sensitivity analysis; Bounded variables technique
11 Transportation problem; Finding an initial basic feasible solution; Transportation simplex method
12 Assignment problem
13 Network models; Terminology of networks; Shortest-path problem; Minimum spanning tree problem
14 Dynamic programming; Principle of optimality; Examples of deterministic dynamic programming
Practice Topics
Week Weekly Contents
Assessment System
Contribution to Overall Grade
  Number Contribution
Contribution of in-term studies to overall grade 3 50
Contribution of final exam to overall grade 1 50
Toplam 4 100
In-Term Studies
  Number Contribution
Assignments 0 0
Presentation 0 0
Midterm Examinations (including preparation) 1 20
Project 0 0
Laboratory 0 0
Other Applications 0 0
Quiz 2 30
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 3 50
Relation of Proficiency
No Program Learning Outcomes Contribution
1 2 3 4 5
1 Knowledge and understanding of a wide range of basic sciences (math, physics, ...) and the main concepts of engineering X
2 Ability to combine the knowledge and skills to solve engineering problems and provide reliable solutions X
3 Ability to select and apply methods of analysis and modeling to ask, reformulate and solve the complex problems of industrial engineering X
4 Ability to conceptualize complex systems, processes or products under practical constraints to improve their performance, ability to use innovative methods of design X
5 Ability to design, select and apply methods and tools needed to solve problems related to the practice of industrial engineering, ability to use computer technology X
6 Ability to design experiments, collect and interpret data and analyze results X
7 Ability to work independently, ability to participate in working groups and have a multidisciplinary team spirit
8 Ability to communicate effectively, ability to speak at least two foreign languages
9 Awareness of the need for continuous improvement of lifelong learning, ability to keep abreast of scientific and technological developments to use the tools of information management X
10 Awareness of professional and ethical responsibility
11 Knowledge of the concepts of professional life as "project management", "risk management" and "management of change" X
12 Knowledge on entrepreneurship, innovation and sustainability
13 Understanding of the effects of Industrial Engineering applications on global and social health, environment and safety.
14 Knowledge of the problems of contemporary society X
15 Knowledge of the legal implications of the practice of industrial engineering
ECTS
Activities Number Period Total Workload
Class Hours 14 4 56
Working Hours out of Class 13 3 39
Midterm Examinations (including preparation) 1 10 10
Final Examinations (including preparation) 1 14 14
Quiz 2 9 18
Total Workload 137
Total Workload / 25 5.48
Credits ECTS 5
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