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Course Description Course Name : Advanced Linear Programming Course Code : IEM 757 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. SEMİN PAKSOY Learning Outcomes of the Course : Gains the ability to perform linear programming techniques Learns to solve the linear problems which has more variables by using the rapid and advanced algorithms Gains the ability to get the optimum solutions for the linear programming problems which have conflicting objectives and multiattributes Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : The aim of the course is to investigate the linear programming in detail and to enable the students to specialize in this issue. The course explains the further linear programming methods which are not mentioned at the undergraduate level and aims to increase the level of knowledge of the student. Students can learn to use powerful and robust analytical methodology that supports the solution to many real-world business problems and fact-based decision making. Course Contents : The course covers linear prolems in matrix form and their solutions, rapid and advanced linear programming solution techniques, parametric linear problems and the effects of the changes in the parameters, karmarkar interior point algorithm and goal programming Language of Instruction : Turkish Work Place : Classroom   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Simplex method for lineer programing Reading relevant parts in the source books according to the weekly program Lecture, problem solving 2 Standard LP model in matrix form, basic solutions and basics Reading relevant parts in the source books according to the weekly program Lecture, problem solving 3 Sensitivity analysis Reading relevant parts in the source books according to the weekly program Direct expression, problem solving 4 Revised simplex tableau Reading relevant parts in the source books according to the weekly program Lecture, problem solving 5 Bounded variables primal simplex method Reading relevant parts in the source books according to the weekly program Lecture, problem solving 6 Decomposition principle Reading relevant parts in the source books according to the weekly program Lecture, problem solving 7 Dual problems in matrix form and their optimum solutions Reading relevant parts in the source books according to the weekly program Lecture, problem solving 8 Midterm exam - - 9 Parametric linear programming Reading relevant parts in the source books according to the weekly program Lecture, problem solving 10 Parametric linear programming Reading relevant parts in the source books according to the weekly program Lecture, problem solving 11 Karmarkar interior point algorithm Reading relevant parts in the source books according to the weekly program Lecture, problem solving 12 Karmarkar Interior point algorithm Reading relevant parts in the source books according to the weekly program Lecture, problem solving 13 Goal programming Reading relevant parts in the source books according to the weekly program Lecture, problem solving 14 Multiobjective optimization Reading relevant parts in the source books according to the weekly program Lecture, problem solving 15 Applications of multiobjective optimization Reading relevant parts in the source books according to the weekly program Lecture, problem solving 16/17 Final exam - -   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Operations Research: an Introduction, Hamdy A. Taha, Macmillian publishing Required Course Material(s)  Introduction to Operations Research, Frederick S. Hiller & Gerald Lieberman   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 70     Homeworks/Projects/Others 10 30 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Explains Econometric concepts 1 2 Equipped with the foundations of Economics, develops Economic models 3 3 Models problems using the knowledge of Mathematics, Statistics, and Econometrics 4 4 Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems 4 5 Collects, edits, and analyzes data 4 6 Uses advanced software packages concerning Econometrics, Statistics, and Operation Research 3 7 Develops the ability to use different resources in an area which has not been studied in the scope of academic rules, synthesizes the information gathered, and gives effective presentations 4 8 Speaks Turkish and at least one other foreign language in accordance with the requirements of academic and business life. 0 9 Questions traditional approaches and their implementation and develops alternative study programs when required 3 10 Recognizes and implements social, scientific, and professional ethic values 4 11 Gives a consistent estimate for the model and analyzes and interprets its results 4 12 Takes responsibility individually and/or as a member of a team; leads a team and works effectively 4 13 Defines the concepts of statistics, operations research and mathematics. 4 14 Knowing the necessity of life-long learning, follows the latest developments in the field of study and improves himself continiously 4 15 Follows the current issues, and interprets the data about economic and social events. 4 16 Understands and interprets the feelings, thoughts and behaviours of people and expresses himself/herself orally and in written form efficiently 4 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 4 56 Assesment Related Works     Homeworks, Projects, Others 10 3 30     Mid-term Exams (Written, Oral, etc.) 1 6 6     Final Exam 1 10 10 Total Workload: 144 Total Workload / 25 (h): 5.76 ECTS Credit: 6