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Course Description Course Name : Mathematical Modelling and Optimization Course Code : EM-511 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Assoc.Prof.Dr. ALİ KOKANGÜL Learning Outcomes of the Course : Constructs mathematical models of real-life problems, selects the most appropriate mathematical modeling method to the constructed mathematical model, validates and verifies the model, and drives the solutions from the model using computer optimization programs such as LINGO. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : The aim of this course is to provide detailed information on mathematical programming techniques and to help students gain ability to apply these techniques using LINDO package program. Course Contents : Mathematical models used in optimization problems, numerical methods for unconstrained optimization problems with one variable, numerical methods for constrained optimization problems with one variable, numerical methods for unconstrained optimization problems with multi-variable, numerical methods for constrained optimization problems with multi-variable, applications of mathematical models,project presentation. Language of Instruction : Turkish Work Place : IE Classroom   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Introduction to mathematical programming reading the related textbook chapter lecture, discussion 2 Mathematical models used in optimization problems, reading the related textbook chapter lecture, discussion 3 Unconstrained mathematical modeling techniques reading the related textbook chapter lecture, discussion 4 Constrained mathematical modeling techniques reading the related textbook chapter lecture, discussion 5 Numerical methods for unconstrained optimization problems with one variable reading the related textbook chapter lecture, discussion 6 Numerical methods for constrained optimization problems with one variable reading the related textbook chapter lecture, discussion 7 Numerical methods for unconstrained optimization problems with multi-variable reading the related textbook chapter lecture, discussion 8 Midterm exam prepare for the exam written exam 9 Numerical methods for unconstrained optimization problems with multi-variable reading the related textbook chapter lecture, discussion 10 Numerical methods for constrained optimization problems with multi-variable reading the related textbook chapter lecture, discussion 11 Numerical methods for constrained optimization problems with multi-variable reading the related textbook chapter lecture, discussion 12 Applications of mathematical models reading the related textbook chapter lecture, discussion 13 Applications of mathematical models reading the related textbook chapter lecture, discussion 14 Project presentation prepare a presentation presentation 15 Project presentation prepare a presentation presentation 16/17 Final exam prepare for the exam written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Operations research application and algorithm, Winston,2004.  Operations research, Taha Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 50     Homeworks/Projects/Others 1 50 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 Understand, interpret and apply knowledge in his/her field domain both in-depth and in-breadth by doing scientific research in industrial engineering. 5 2 Acquire comprehensive knowledge about methods and tools of industrial engineering and their limitations. 4 3 Work in multi-disciplinary teams and take a leading role and responsibility. 3 4 Identify, gather and use necessary information and data. 4 5 Complete and apply the knowledge by using scarce and limited resources in a scientific way and integrate the knowledge into various disciplines. 4 6 Keep up with the recent changes and applications in the field of Industrial Engineering and analyze these innovations when necessary. 5 7 Work in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. 4 8 Analyze Industrial Engineering problems, develop innovative methods to solve the problems. 5 9 Have the ability to propose new and/or original ideas and methods in developing innovative solutions for designing systems, components or processes. 4 10 Design and perform analytical modeling and experimental research and analyze/solve complex matters emerged in this process. 5 11 Follow, study and learn new and developing applications of industrial engineering. 4 12 Use a foreign language in verbal and written communication at least B2 level of European Language Portfolio. 2 13 Present his/her research findings systematically and clearly in oral and written forms in national and international platforms. 3 14 Understand social and environmental implications of engineering practice. 5 15 Consider social, scientific and ethical values in the process of data collection, interpretation and announcement of the findings. 5 * 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 6 84 Assesment Related Works     Homeworks, Projects, Others 1 4 4     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 10 10 Total Workload: 150 Total Workload / 25 (h): 6 ECTS Credit: 6