COURSE SYLLABUS

OPERATIONS RESEARCH I

1	Course Title:	OPERATIONS RESEARCH I
2	Course Code:	END3033
3	Type of Course:	Compulsory
4	Level of Course:	First Cycle
5	Year of Study:	3
6	Semester:	5
7	ECTS Credits Allocated:	5
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	1
11	Prerequisites:	Introduction to Mathematical Programming
12	Recommended optional programme components:	None
13	Language:	English
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Doç. Dr. Fatih ÇAVDUR
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	e-posta: fatihcavdur@uludag.edu.tr, Telefon: + 90 (224) 294 20 77 Adress: Uludağ Üniversitesi, Mühendislik-Mimarlık Fakültesi, Endüstri Mühendisliği Bölümü, Görükle Kampüsü, 16059 Nilüfer, Bursa
18	Website:
19	Objective of the Course:	Learning operations research techniques, and finding the best solution using the building-up analytical thinking approach.
20	Contribution of the Course to Professional Development

Learning Outcomes:

1	Being able to solve linear programming problems using the simplex / atrificial starting solution / two phase simplex methods.;
2	Having knowledge about special cases of the simplex algorithm, and being able to interpret the solutions and results.;
3	Being able to perform sensitivity analysis on the solutions of linear programming models.;
4	Being able to find the dual of a problem, and to interpret the economic meaning of the solution. Being able to use dual simplex.;
5	Being able to model and solve integer programming problems.;
6	Being able to model and solve goal programming problems.;

22	Course Content:

Week	Theoretical	Practical
1	Introduction Solution of Linear Programming Problems: Simplex Method -Standard and canonical forms -Introduction to simplex algorithm	Using LINDO for modeling linear programming problems.
2	Solving linear programming problems using simplex algorithm.	Using LINDO for the solution of linear programming problems, and interpreting results.
3	Artifical Starting Solution (Big M Method) Yapay Başlangıç Yöntemi (Büyük M Yöntemi)	Solving MS Excel Solver for modeling and solving linear programming problems, interpreting results.
4	Two-Phase Simplex Method	Solving various linear programming problems and interpreting results.
5	Special Cases of Simplex Algorithm -Degeneracy -Infeasibility -Unbounded Solution Simplex algorithm for unbounded variables	Analyzing special cases of simplex algorithms with examples.
6	Sensitivity Analysis	Sensitivity analysis practices.
7	Sensitivity Analysis -Objective function coefficient changes -Right hand side changes	Sensitivity analysis practices.
8	Sensitivity Analysis -Objective function coefficient changes -Right hand side changes	Sensitivity analysis practices.
9	Duality Primal / Dual Problems / Variables Primal / Dual Transformation Relations between the Primal / Dual Solutions	Examples about primal/dual transformations / solutions.
10	Dual simplex algorithm Duality and Sensitivity analysis Economic interpretation of the dual solutions-shadow prices-reduced costs	Economic interpretation of the dual solution
11	Integer Programming Analyzing various integer programming problems.	Modeling, solving and interpreting results of integer programming problems using MPL.
12	Solving integer programming problems -Branch and bound algorithm -Cutting plane algorithm	Analyzing various integer programming problems.
13	Goal Programming Analyzing various goal programming problems	Modeling, solving and interpreting results of goal programming problems using MPL.
14	Solving goal programming problems -Preemptive goal programming -Non-preemptive goal programming	Analyzing various goal programming problems.

23	Textbooks, References and/or Other Materials:	1. Winston, W.L., Operations Research: Applications and Algorithms, 4th ed., Brooks/Cole-Thomson Learning, 2004. 2. Hillier, F.S.; Lieberman, G.J., Introduction to Operations Research, 9th ed., McGraw Hill, Boston, 2005.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	30
Quiz	0	0
Homeworks, Performances	3	20
Final Exam	1	50
Total	5	100
Contribution of Term (Year) Learning Activities to Success Grade		50
Contribution of Final Exam to Success Grade		50
Total		100
Measurement and Evaluation Techniques Used in the Course
Information

ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	14	1	14
Self Study and Preparation	14	5	70
Homeworks, Performances	3	5	5
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	7	7
Others	1	5	5
Final Exams	1	7	7
Total WorkLoad			157
Total workload/ 30 hr			5
ECTS Credit of the Course			5

CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS

	PQ1	PQ2	PQ3	PQ4	PQ5	PQ6	PQ7	PQ8	PQ9	PQ10	PQ11	PQ12	PQ13	PQ14	PQ15
LO1	5	5	3	3	1	1	1	1	1	1	1	1	1	1	1
LO2	5	5	3	3	1	1	1	1	1	1	1	1	1	1	1
LO3	5	5	3	4	1	1	1	1	1	1	1	1	1	1	1
LO4	5	5	3	4	1	1	1	1	1	1	1	1	1	1	1
LO5	5	5	3	4	1	1	1	1	1	1	1	1	1	1	1
LO6	5	5	3	4	1	1	1	1	1	1	1	1	1	1	1

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High