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. BURCU ÇAĞLAR GENÇOSMAN
16	Course Lecturers:	Doç.Dr. Burcu ÇAĞLAR GENÇOSMAN
17	Contactinformation of the Course Coordinator:	e-posta: burcucaglar@uludag.edu.tr, Telefon: + 90 (224) 294 09 16 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	It's been planned to contribute to professional development by analyzing real life problems by scientific methods and offering solutions.

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 model and solve transportation problems and assignment problems.;
5	Being able to model and solve network problems and CPM.;

22	Course Content:

Week	Theoretical	Practical
1	Introduction Solution of Linear Programming Problems: Simplex Method -Standard and canonical forms -Introduction to simplex algorithm	Using MS Excel Solver for modeling linear programming problems.
2	Solving linear programming problems using simplex algorithm.	Using MS Excel Solver for the solution of linear programming problems, and interpreting results.
3	Artifical Starting Solution (Big M Method)	Using Lindo for modeling and solving linear programming problems, interpreting results.
4	Two-Phase Simplex Method	Using Lindo for modeling and solving linear programming problems, interpreting results.
5	Special Cases of Simplex Algorithm -Degeneracy -Infeasibility -Unbounded Solution Simplex algorithm for unbounded variables	Sensitivity analysis practices in Lindo
6	Sensitivity Analysis -Objective function coefficient changes -Right hand side changes	Sensitivity analysis practices in Lindo
7	Sensitivity Analysis -Objective function coefficient changes -Right hand side changes	How to download setup and use IBM ILOG Cplex Optimization Studio software
8	Duality Primal / Dual Problems / Variables Primal / Dual Transformation Relations between the Primal / Dual Solutions/Complementary Slackness theorem	How to use IBM ILOG Cplex Optimization Studio software
9	Introduction to transportation problems /balanced transportation problem / finding basic feasible solutions of transportation problems	Representation of parameters, decision variables and constraints in IBM ILOG Cplex Optimization Studio and some examples
10	Transportation simplex algorithm	Representation of parameters, decision variables and constraints in IBM ILOG Cplex Optimization Studio and some examples
11	Assignment problems /Hungarian algorithm	Representation of parameters, decision variables and constraints in IBM ILOG Cplex Optimization Studio and some examples
12	-Network problems examples / Shortest path problems/Floyd algorithm/Dijkstra algorithm	Modeling, solving and interpreting results of linear programming problems using CPLEX
13	Minimum spanning tree problems /Maximum flow problems/CPM	Modeling, solving and interpreting results of linear programming problems using CPLEX
14	Review studies of OR I topics with examples	Modeling, solving and interpreting results of linear programming problems using CPLEX

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	5	10
Final Exam	1	60
Total	7	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	1 Midterm Exam + 4 Homeworks + 1 Term Project + 1 Final Exam
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	5	5	10
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	7	7
Others	0	0	0
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	PQ23	PQ24
LO1	5	5	5	5	5	5	5	5	5	2	2	1	1	1	5	5
LO2	4	4	4	4	2	2	4	4	4	2	2	1	1	1	4	4
LO3	3	3	2	2	2	2	4	4	4	2	2	1	1	1	4	4
LO4	5	5	5	5	5	5	5	5	5	2	2	1	1	1	5	5
LO5	5	5	5	5	5	5	5	5	5	2	2	1	1	1	5	5

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High